Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
143 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Minimizing Regret in Billboard Advertisement under Zonal Influence Constraint (2402.01294v1)

Published 2 Feb 2024 in cs.DB, cs.IR, and cs.MA

Abstract: In a typical billboard advertisement technique, a number of digital billboards are owned by an influence provider, and many advertisers approach the influence provider for a specific number of views of their advertisement content on a payment basis. If the influence provider provides the demanded or more influence, then he will receive the full payment or else a partial payment. In the context of an influence provider, if he provides more or less than an advertiser's demanded influence, it is a loss for him. This is formalized as 'Regret', and naturally, in the context of the influence provider, the goal will be to allocate the billboard slots among the advertisers such that the total regret is minimized. In this paper, we study this problem as a discrete optimization problem and propose four solution approaches. The first one selects the billboard slots from the available ones in an incremental greedy manner, and we call this method the Budget Effective Greedy approach. In the second one, we introduce randomness with the first one, where we perform the marginal gain computation for a sample of randomly chosen billboard slots. The remaining two approaches are further improvements over the second one. We analyze all the algorithms to understand their time and space complexity. We implement them with real-life trajectory and billboard datasets and conduct a number of experiments. It has been observed that the randomized budget effective greedy approach takes reasonable computational time while minimizing the regret.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (30)
  1. Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Transactions on Intelligent Systems and Technology (TIST) 13(2), 1–23 (2022) Zhang et al. [2020] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Towards an optimal outdoor advertising placement: when a budget constraint meets moving trajectories. ACM Transactions on Knowledge Discovery from Data (TKDD) 14(5), 1–32 (2020) Wang et al. [2019] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE transactions on industrial informatics 16(2), 1058–1066 (2019) Zhang et al. [2019] Zhang, Y., Bao, Z., Mo, S., Li, Y., Zhou, Y.: Itaa: An intelligent trajectory-driven outdoor advertising deployment assistant. Proc. VLDB Endow. 12(12), 1790–1793 (2019) https://doi.org/10.14778/3352063.3352067 Lotfi et al. [2017] Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Towards an optimal outdoor advertising placement: when a budget constraint meets moving trajectories. ACM Transactions on Knowledge Discovery from Data (TKDD) 14(5), 1–32 (2020) Wang et al. [2019] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE transactions on industrial informatics 16(2), 1058–1066 (2019) Zhang et al. [2019] Zhang, Y., Bao, Z., Mo, S., Li, Y., Zhou, Y.: Itaa: An intelligent trajectory-driven outdoor advertising deployment assistant. Proc. VLDB Endow. 12(12), 1790–1793 (2019) https://doi.org/10.14778/3352063.3352067 Lotfi et al. [2017] Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE transactions on industrial informatics 16(2), 1058–1066 (2019) Zhang et al. [2019] Zhang, Y., Bao, Z., Mo, S., Li, Y., Zhou, Y.: Itaa: An intelligent trajectory-driven outdoor advertising deployment assistant. Proc. VLDB Endow. 12(12), 1790–1793 (2019) https://doi.org/10.14778/3352063.3352067 Lotfi et al. [2017] Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Bao, Z., Mo, S., Li, Y., Zhou, Y.: Itaa: An intelligent trajectory-driven outdoor advertising deployment assistant. Proc. VLDB Endow. 12(12), 1790–1793 (2019) https://doi.org/10.14778/3352063.3352067 Lotfi et al. [2017] Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  2. Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Towards an optimal outdoor advertising placement: when a budget constraint meets moving trajectories. ACM Transactions on Knowledge Discovery from Data (TKDD) 14(5), 1–32 (2020) Wang et al. [2019] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE transactions on industrial informatics 16(2), 1058–1066 (2019) Zhang et al. [2019] Zhang, Y., Bao, Z., Mo, S., Li, Y., Zhou, Y.: Itaa: An intelligent trajectory-driven outdoor advertising deployment assistant. Proc. VLDB Endow. 12(12), 1790–1793 (2019) https://doi.org/10.14778/3352063.3352067 Lotfi et al. [2017] Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE transactions on industrial informatics 16(2), 1058–1066 (2019) Zhang et al. [2019] Zhang, Y., Bao, Z., Mo, S., Li, Y., Zhou, Y.: Itaa: An intelligent trajectory-driven outdoor advertising deployment assistant. Proc. VLDB Endow. 12(12), 1790–1793 (2019) https://doi.org/10.14778/3352063.3352067 Lotfi et al. [2017] Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Bao, Z., Mo, S., Li, Y., Zhou, Y.: Itaa: An intelligent trajectory-driven outdoor advertising deployment assistant. Proc. VLDB Endow. 12(12), 1790–1793 (2019) https://doi.org/10.14778/3352063.3352067 Lotfi et al. [2017] Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  3. Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE transactions on industrial informatics 16(2), 1058–1066 (2019) Zhang et al. [2019] Zhang, Y., Bao, Z., Mo, S., Li, Y., Zhou, Y.: Itaa: An intelligent trajectory-driven outdoor advertising deployment assistant. Proc. VLDB Endow. 12(12), 1790–1793 (2019) https://doi.org/10.14778/3352063.3352067 Lotfi et al. [2017] Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Bao, Z., Mo, S., Li, Y., Zhou, Y.: Itaa: An intelligent trajectory-driven outdoor advertising deployment assistant. Proc. VLDB Endow. 12(12), 1790–1793 (2019) https://doi.org/10.14778/3352063.3352067 Lotfi et al. [2017] Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  4. Zhang, Y., Bao, Z., Mo, S., Li, Y., Zhou, Y.: Itaa: An intelligent trajectory-driven outdoor advertising deployment assistant. Proc. VLDB Endow. 12(12), 1790–1793 (2019) https://doi.org/10.14778/3352063.3352067 Lotfi et al. [2017] Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  5. Lotfi, R., Mehrjerdi, Y.Z., Mardani, N.: A multi-objective and multi-product advertising billboard location model with attraction factor mathematical modeling and solutions. International journal of applied logistics (IJAL) 7(1), 64–86 (2017) Zhang et al. [2021] Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  6. Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021) Ali et al. [2022] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  7. Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230 (2022). Springer Ali et al. [2023] Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  8. Ali, D., Bhagat, A.K., Banerjee, S., Prasad, Y.: Efficient algorithms for regret minimization in billboard advertisement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16148–16149 (2023) Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  9. Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205–1215 (2019) Cabello et al. [2006] Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  10. Cabello, S., Langerman, S., Seara, C., Ventura, I.: Reverse Facility Location Problems. Citeseer, ??? (2006) Cabello et al. [2010] Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  11. Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Facility location problems in the plane based on reverse nearest neighbor queries. European Journal of Operational Research 202(1), 99–106 (2010) Du et al. [2005] Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  12. Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, pp. 163–180. Springer, Berlin, Heidelberg (2005) Xia et al. [2005] Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  13. Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB ’05, pp. 946–957. VLDB Endowment, ??? (2005) Stanoi et al. [2001] Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  14. Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases. VLDB ’01, pp. 99–108. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001) Zhou et al. [2011] Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  15. Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: Maxfirst for maxbrknn. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 828–839 (2011). https://doi.org/10.1109/ICDE.2011.5767892 Huang et al. [2011] Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  16. Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. CIKM ’11, pp. 2377–2380. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063971 . https://doi.org/10.1145/2063576.2063971 Wang et al. [2021] Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  17. Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2) (2021) https://doi.org/10.1145/3440207 Guo et al. [2017] Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  18. Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.-L.: Influence maximization in trajectory databases. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 27–28 (2017). https://doi.org/10.1109/ICDE.2017.20 Zhang et al. [2019] Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  19. Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 1205–1215. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330829 . https://doi.org/10.1145/3292500.3330829 Wang et al. [2020] Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  20. Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Transactions on Industrial Informatics 16(2), 1058–1066 (2020) https://doi.org/10.1109/TII.2019.2891258 Zahrádka et al. [2021] Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  21. Zahrádka, J., Machová, V., Kučera, J.: What is the price of outdoor advertising: A case study of the czech republic? Ad Alta: Journal of Interdisciplinary Research (2021) Zhang et al. [2018] Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  22. Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748–2757 (2018) Ali et al. [2023] Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  23. Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023) Wang et al. [2022] Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  24. Wang, L., Yu, Z., Guo, B., Yang, D., Ma, L., Liu, Z., Xiong, F.: Data-driven targeted advertising recommendation system for outdoor billboard. ACM Trans. Intell. Syst. Technol. 13(2) (2022) https://doi.org/10.1145/3495159 Liu et al. [2017] Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  25. Liu, D., Weng, D., Li, Y., Bao, J., Zheng, Y., Qu, H., Wu, Y.: Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics 23, 1–10 (2017) Nanongkai et al. [2010] Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  26. Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. Proc. VLDB Endow. 3(1–2), 1114–1124 (2010) https://doi.org/10.14778/1920841.1920980 Peng and Wong [2014] Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  27. Peng, P., Wong, R.C.-W.: Geometry approach for k-regret query. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 772–783 (2014). https://doi.org/10.1109/ICDE.2014.6816699 Xie et al. [2018] Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  28. Xie, M., Wong, R.C.-W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of the 2018 International Conference on Management of Data. SIGMOD ’18, pp. 959–974. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3183713.3196903 . https://doi.org/10.1145/3183713.3196903 Xie et al. [2019] Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  29. Xie, M., Wong, R.C.-W., Lall, A.: Strongly truthful interactive regret minimization. In: Proceedings of the 2019 International Conference on Management of Data. SIGMOD ’19, pp. 281–298. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3299869.3300068 . https://doi.org/10.1145/3299869.3300068 Aslay et al. [2015] Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950 Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
  30. Aslay, C., Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: Viral marketing meets social advertising: Ad allocation with minimum regret. Proc. VLDB Endow. 8(7), 814–825 (2015) https://doi.org/10.14778/2752939.2752950
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now