Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Learning Service Selection Decision Making Behaviors During Scientific Workflow Development (2404.00420v1)

Published 30 Mar 2024 in cs.SE and cs.LG

Abstract: Increasingly, more software services have been published onto the Internet, making it a big challenge to recommend services in the process of a scientific workflow composition. In this paper, a novel context-aware approach is proposed to recommending next services in a workflow development process, through learning service representation and service selection decision making behaviors from workflow provenance. Inspired by natural language sentence generation, the composition process of a scientific workflow is formalized as a step-wise procedure within the context of the goal of workflow, and the problem of next service recommendation is mapped to next word prediction. Historical service dependencies are first extracted from scientific workflow provenance to build a knowledge graph. Service sequences are then generated based on diverse composition path generation strategies. Afterwards, the generated corpus of composition paths are leveraged to study previous decision making strategies. Such a trained goal-oriented next service prediction model will be used to recommend top K candidate services during workflow composition process. Extensive experiments on a real-word repository have demonstrated the effectiveness of this approach.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. A. L. Lemos, F. Daniel, and B. Benatallah, “Web service composition: a survey of techniques and tools”, ACM Computing Surveys (CSUR), vol. 48, no. 3, pp. 1-41, 2015.
  2. W. Tan, J. Zhang, & I. Foster, “Network Analysis of Scientific Workflows: A Gateway to Reuse”, Computer, 43(9), 2010, pp. 54-61.
  3. C. A. Goble, J. Bhagat, S. Aleksejevs, D. Cruickshank, D. Michaelides, D. Newman, M. Borkum, S. Bechhofer, M. Roos, P. Li, & D. D. Roure, “myExperiment: A Repository and Social Network for the Sharing of Bioinformatics Workflows”, Nucleic Acids Research, 38, 2010, pp. W677-W682.
  4. J. Zhang, M. Pourreza, S. Lee, R. Nemani, & T. J. Lee, “Unit of Work Supporting Generative Scientific Workflow Recommendation”, in Proceedings of International Conference on Service-Oriented Computing, Nov. 2018, pp. 446-462.
  5. J. Zhang, W. Wang, X. Wei, C. Lee, S. Lee, L. Pan, & T. J. Lee, “Climate Analytics Workflow Recommendation as A Service-provenance-driven Automatic Workflow Mashup”, in Proceedings of IEEE International Conference on Web Services, 2015, pp. 89-97.
  6. D. Hull, R. Stevens, P. Lord, C. Wroe, & C. Goble, “Treating Shimantic Web Syndrome With Ontologies”, in Proceedings of The 1st AKT Workshop on Semantic Web Services (AKT-SWS04), Dec. 2004, pp. 1-4.
  7. J. Zhang, W. Tan, J. Alexander, I. Foster, & R. Madduri, “Recommend-as-you-go: A Novel Approach Supporting Services-oriented Scientific Workflow Reuse”, in Proceedings of IEEE International Conference on Services Computing, Jul. 2011, pp. 48-55.
  8. G. Shani, D. Heckerman, & R. I. Brafman, “An MDP-based Recommender System”, Journal of Machine Learning Research, 6(9), 2005, pp. 1265-1295.
  9. F. Yu, Q. Liu, S. Wu, L. Wang, & T. Tan, “A Dynamic Recurrent Model for Next Basket Recommendation”, in Proceedings of The 39th ACM SIGIR International Conference on Research and Development in Information Retrieval, Jul. 2016, pp. 729-732.
  10. A. Hard, K. Rao, R. Mathews, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, & D. Ramage, “Federated Learning for Mobile Keyboard Prediction”, arXiv, arXiv:1811.03604, 2018.
  11. Y. Zhang, H. Dai, C. Xu, J. Feng, T. Wang, J. Bian, B. Wang, & T. Y. Liu, “Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks”, AAAI, 28(1), Jun. 2014, pp. 1369-1376.
  12. S. Rendle, C. Freudenthaler, & L. Schmidt-Thieme, “Factorizing Personalized Markov Chains for Next-basket Recommendation”, in Proceedings of The 19th International Conference on World Wide Web, Apr. 2010, pp. 811-820.
  13. B. Hidasi, A. Karatzoglou, L. Baltrunas, & D. Tikk, “Session-based Recommendations with Recurrent Neural Networks”, In Proceedings of the 4th International Conference on Learning Representations, 2016, pp. 1–10.
  14. T. Mikolov, K. Chen, G. Corrado, & J. Dean, “Efficient Estimation of Word Representations in Vector Space”, arXiv preprint, arXiv:1301.3781, 2013.
  15. I. Paik, W. Chen, & M. N. Huhns, “A Scalable Architecture for Automatic Service Composition”, IEEE Transactions on Services Computing, 7(1), 2012, pp. 82-95.
  16. B. Xia, Y. Fan, W. Tan, K. Huang, J. Zhang, & C. Wu, “Category-aware API Clustering and Distributed Recommendation for Automatic Mashup Creation”, IEEE Transactions on Services Computing, 8(5), 2014, pp. 674-687.
  17. D. Koop, C. E. Scheidegger, S. P. Callahan, J. Freire, & C. T. Silva, “Viscomplete: Automating Suggestions for Visualization Pipelines”, IEEE Transactions on Visualization and Computer Graphics, 14(6), 2008, pp. 1691-1698.
  18. S. Deng, D. Wang, Y. Li, B. Cao, J. Yin, Z. Wu, & M. Zhou, “A Recommendation System to Facilitate Business Process Modeling”, IEEE Transactions on Cybernetics, 47(6), 2016, pp. 1380-1394.
  19. J. Zhang, Q. Liu, & K. Xu, “FlowRecommender: A Workflow Recommendation Technique for Process Provenance”, in Proceedings of The 8th Australasian Data Mining Conference, 2009, pp. 55-61.
  20. S. Smirnov, M. Weidlich, J. Mendling, & M. Weske, “Action Patterns in Business Process Models”, in Proceedings of The 7th International Conference on Service-Oriented computing, 2009, pp. 115-129.
  21. C. Li, R. Zhang, J. Huai, X. Guo, & H. Sun, “A Probabilistic Approach for Web Service Discovery”, in Proceedings of IEEE International Conference on Services Computing, 2013, pp. 49-56.
  22. Y. Zhong, Y. Fan, W. Tan, & J. Zhang, “Web Service Recommendation with Reconstructed Profile from Mashup Descriptions”, IEEE Transactions on Automation Science and Engineering, 15(2), 2016, pp. 468-478.
  23. J. Zhang, Y. Fan, J. Zhang, & B. Bai, “Learning to Build Accurate Service Representations and Visualization”, IEEE Transactions on Services Computing, Jun. 2020.
  24. X. Xie, J. Zhang, R. Ramachandran, T. J. Lee, & S. Lee, (2022). “Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition”, in Proceedings of IEEE/ACIS 22nd International Hybrid Conference on Computer and Information Science, 2022, pp. 60-65.
  25. A. Schofield, L. Thompson, & D. Mimno, “Quantifying the Effects of Text Duplication on Semantic Models”, in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, pp. 2737-2747.
  26. B. Perozzi, R. Al-Rfou, & S. Skiena, “Deepwalk: Online Learning of Social Representations”, in Proceedings of The 20th ACM International Conference on Knowledge Discovery and Data Mining, Aug. 2014, pp. 701-710.
  27. Y. Zhang, M. Zhang, X. Zheng, & D. E. Perry, “Service2vec: A Vector Representation for Web Services”, in Proceedings of IEEE International Conference on Web Services, Jun. 2017, pp. 890-893.
  28. H. Mezni, D. Benslimane, L. Belletreche, “Context-aware Service Recommendation based on Knowledge Graph Embedding”. IEEE Transactions on Knowledge and Data Engineering, 34(11), 2022, pp. 5225-5238.
  29. Z. Zhang, O. Nasraoui, “Efficient Hybrid Web Recommendations based on Markov Clickstream Models and Implicit Search”, in Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence, 2007, pp. 621-627.
  30. F. Garcin, C. Dimitrakakis, B. Faltings, “Personalized News Recommendation with Context Trees”, in Proceedings of the 7th ACM Conference on Recommender Systems, 2013, pp. 105–112.
  31. M. Shi, Y. Tang, J. Liu, “Functional and Contextual Attention-based LSTM for Service Recommendation in Mashup Creation”. IEEE Transactions on Parallel and Distributed Systems, 30(5), May 2015, pp. 1077-1090.
  32. C. Wu, A. Ahmed, A. Beutel, A. J. Smola, H. Jing, “Recurrent Recommender Networks”, in Proceedings of the 10th ACM International Conference on Web Search and Data Mining, 2017, pp. 495-503.
  33. S. Hochreiter, J. Schmidhuber, “Long short-term memory”. Neural Computation, 9(8), 1997, pp. 1735-1780.
  34. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, J. Dean, “Distributed Representations of Words and Phrases and Their Compositionality”, in Proceedings of The 26th International Conference on Neural Information Processing Systems, 2013, pp. 3111–3119.
  35. K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, J. Schmidhuber, “LSTM: A Search Space Odyssey”. IEEE Transactions on Neural Networks and Learning Systems, 28(10), Oct. 2017, pp. 2222-2232.
  36. X. Wang, X. Liu, J. Liu, X. Chen, H. Wu, “A Novel Knowledge Graph Embedding based API Recommendation Method for Mashup Development”, World Wide Web, 24(3), 2021, pp. 869-894.
  37. S. Hochreiter, J. Schmidhuber, “Long Short-Term Memory.” Neural Computation, 9(8), 1995, pp. 1735–1780.
  38. Le, Q., Mikolov, T., “Distributed Representations of Sentences and Documents,” in Proceedings of IEEE International Conference on Machine Learning, 2014, pp. 1188–1196
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Xihao Xie (4 papers)
  2. Jia Zhang (118 papers)
  3. Rahul Ramachandran (17 papers)
  4. Tsengdar J. Lee (5 papers)
  5. Seungwon Lee (14 papers)

Summary

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

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

Tweets