2000 character limit reached
Towards Model-Driven Dashboard Generation for Systems-of-Systems (2402.15257v1)
Published 23 Feb 2024 in cs.SE
Abstract: Configuring and evolving dashboards in complex and large-scale Systems-of-Systems (SoS) can be an expensive and cumbersome task due to the many Key Performance Indicators (KPIs) that are usually collected and have to be arranged in a number of visualizations. Unfortunately, setting up dashboards is still a largely manual and error-prone task requiring extensive human intervention. This short paper describes emerging results about the definition of a model-driven technology-agnostic approach that can automatically transform a simple list of KPIs into a dashboard model, and then translate the model into an actual dashboard for a target dashboard technology. Dashboard customization can be efficiently obtained by solely modifying the abstract model representation, freeing operators from expensive interactions with actual dashboards.
- J. Díaz, J. Pérez, J. Pérez, and J. Garbajosa, “Conceptualizing a framework for cyber-physical systems of systems development and deployment,” in European Conference on Software Architecture Workshops, 2016.
- O. Carlsson, C. Hegedűs, J. Delsing, and P. Varga, “Organizing iot systems-of-systems from standardized engineering data,” in Annual Conference of the IEEE Industrial Electronics Society, 2016.
- J. Lukkien, “A systems of systems perspective on the internet of things,” ACM SIGBED Review, vol. 13, no. 3, pp. 56–62, 2016.
- A. Morkevicius, L. Bisikirskiene, and G. Bleakley, “Using a systems of systems modeling approach for developing industrial internet of things applications,” in System of Systems Engineering Conference, 2017.
- G. Fortino, C. Savaglio, G. Spezzano, and M. Zhou, “Internet of things as system of systems: A review of methodologies, frameworks, platforms, and tools,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 1, pp. 223–236, 2021.
- M. Vierhauser, R. Rabiser, P. Grünbacher, K. Seyerlehner, S. Wallner, and H. Zeisel, “Reminds: A flexible runtime monitoring framework for systems of systems,” Journal of Systems and Software, vol. 112, pp. 123–136, 2016.
- L. M. Kritzinger, T. Krismayer, M. Vierhauser, R. Rabiser, and P. Grünbacher, “Visualization support for requirements monitoring in systems of systems,” in 32nd IEEE/ACM International Conference on Automated Software Engineering, 2017.
- R. Rabiser, J. Thanhofer-Pilisch, M. Vierhauser, P. Grünbacher, and A. Egyed, “Developing and evolving a dsl-based approach for runtime monitoring of systems of systems,” Automated Software Engineering, vol. 25, pp. 875–915, 2018.
- G. Labs, “Grafana,” https://grafana.com/, 2023, accessed: 2023.
- E. B.V., “Kibana,” https://www.elastic.co/kibana, 2023, accessed: 2023.
- M. Chakraborty and A. P. Kundan, “Grafana,” in Monitoring Cloud-Native Applications: Lead Agile Operations Confidently Using Open Source Software, 2021, pp. 187–240.
- T. Dodds, “Elasticsearch dashboard,” https://grafana.com/grafana/dashboards/878-elasticsearch-dashboard/, 2018, accessed: 2023.
- J. M. Alcaraz Calero and J. G. Aguado, “Monpaas: An adaptive monitoring platformas a service for cloud computing infrastructures and services,” IEEE Transactions on Services Computing, vol. 8, no. 1, pp. 65–78, 2015.
- D. Trihinas, G. Pallis, and M. D. Dikaiakos, “Monitoring elastically adaptive multi-cloud services,” IEEE Transactions on Cloud Computing, vol. 6, no. 3, pp. 800–814, 2018.
- P. Authors, “Http sd,” https://prometheus.io/docs/prometheus/latest/http_sd/, 2023, accessed: 2023.
- A. Tundo, M. Mobilio, O. Riganelli, and L. Mariani, “Automated probe life-cycle management for monitoring-as-a-service,” IEEE Transactions on Services Computing, vol. 16, no. 2, pp. 969–982, 2023.
- V. Colombo, A. Tundo, M. Ciavotta, and L. Mariani, “Towards self-adaptive peer-to-peer monitoring for fog environments,” in Symposium on Software Engineering for Adaptive and Self-Managing Systems, 2022.
- A. Tundo, C. Castelnovo, M. Mobilio, O. Riganelli, and L. Mariani, “Declarative dashboard generation,” in IEEE International Symposium on Software Reliability Engineering Workshops, 2020.
- A. Vázquez-Ingelmo, F. García-Peñalvo, R. Therón, D. Amo-Filva, and D. Fonseca, “Connecting domain-specific features to source code: towards the automatization of dashboard generation,” Cluster Computing, vol. 23, p. 1803–1816, 2020.
- M. Kintz, M. Kochanowski, and F. Koetter, “Creating user-specific business process monitoring dashboards with a model-driven approach,” in International Conference on Model-Driven Engineering and Software Development, 2017.
- L. Erazo-Garzon, K. Quinde, A. Bermeo, and P. Cedillo, “A domain-specific language and model-based engine for implementing iot dashboard web applications,” in Information and Communication Technologies, 2023.
- O. Belo, P. Rodrigues, R. Barros, and H. Correia, “Restructuring dynamically analytical dashboards based on usage profiles,” in International Symposium on Foundations of Intelligent Systems, 2014.
- I. Dabbebi, S. Iksal, J. Gilliot, M. May, and S. Garlatti, “Towards adaptive dashboards for learning analytic: An approach for conceptual design and implementation,” in International Conference on Computer Supported Education, 2017.
- H. Santos, V. Dantas, V. Furtado, P. Pinheiro, and D. L. McGuinness, “From data to city indicators: A knowledge graph for supporting automatic generation of dashboards,” in Extended Semantic Web Conference, 2017.
- S. Da Col, R. Ciucanu, M. Soare, N. Bouarour, and S. Amer-Yahia, “Dashbot: An ml-guided dashboard generation system,” in ACM International Conference on Information & Knowledge Management, 2021.
- C. Diamantini, D. Potena, and E. Storti, “Sempi: A semantic framework for the collaborative construction and maintenance of a shared dictionary of performance indicators,” Future Generation Computer Systems, vol. 54, pp. 352–365, 2016.
- P. Authors, “Prometheus node exporter,” https://github.com/prometheus/node_exporter, 2023, accessed: 2023.
- ——, “Prometheus,” https://prometheus.io/, 2023, accessed: 2023.
- D. Deng, A. Wu, H. Qu, and Y. Wu, “Dashbot: Insight-driven dashboard generation based on deep reinforcement learning,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 690–700, 2023.