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Incquery server for teamwork cloud: scalable query evaluation over collaborative model repositories

Published:14 October 2018Publication History

ABSTRACT

Large-scale cyber-physical systems are co-engineered, especially in safety-critical industries, by various specialists within an organization and, increasingly, across organizations. The collaborative aspect of the process is facilitated by hosting engineering artifacts in model repositories. In order to validate the adherence to design rules, perform change impact analysis across projects, generate reports etc., engineers specify model queries and evaluate them using query engines, traditionally available in client modeling tools.

In this paper we introduce IncQuery Server for Teamwork Cloud (IQS4TWC), a standalone middleware service that connects to Teamwork Cloud model repositories, and builds on Viatra Query to provide fast querying over their content. The new server-side solution provides advanced features including single-model ad-hoc queries as well as repository-wide change impact analysis (correlating projects across branches and revisions); access to version snapshots as well as queries on the latest state; and a range of performance fine-tuning options (such as elasticity and in-memory indexes) to achieve high scalability.

References

  1. Axellience. {n. d.}. GenMyModel. http://www.genmymodel.com.Google ScholarGoogle Scholar
  2. Hugo Bruneliere, Florent Marchand de Kerchove, Gwendal Daniel, and Jordi Cabot. 2018. Towards Scalable Model Views on Heterogeneous Model Resources. In ACM/IEEE 21th International Conference on Model Driven Engineering Languages and Systems (MODELS '18). Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Gwendal Daniel, Gerson Sunyé, Amine Benelallam, Massimo Tisi, Yoann Vernageau, Abel Gomez, and Jordi CABOT. 2017. NeoEMF: A Multi-database Model Persistence Framework for Very Large Models. Science of Computer Programming (Aug. 2017). Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Gwendal Daniel, Gerson Sunyé, and Jordi Cabot. 2018. Scalable Queries and Model Transformations with the Mogwai Tool. In Theory and Practice of Model Transformation. Springer International Publishing, Cham, 175--183.Google ScholarGoogle Scholar
  5. Csaba Debreceni, Gábor Bergmann, István Ráth, and Dániel Varró. 2017. Enforcing fine-grained access control for secure collaborative modelling using bidirectional transformations. Software & Systems Modeling (21 Nov 2017).Google ScholarGoogle Scholar
  6. Docker Inc. {n. d.}. Docker. https://www.docker.com.Google ScholarGoogle Scholar
  7. Antonio Garcia-Dominguez, Konstantinos Barmpis, Dimitrios S Kolovos, Marcos Aurelio Almeida da Silva, Antonin Abherve, and Alessandra Bagnato. 2016. Integration of a Graph-based Model Indexer in Commercial Modelling Tools. In Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems (MODELS '16). ACM, New York, NY, USA, 340--350. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Antonio Garcia-Dominguez, Konstantinos Barmpis, Dimitrios S. Kolovos, Ran Wei, and Richard F. Paige. 2017. Stress-testing remote model querying APIs for relational and graph-based stores. Software & Systems Modeling (30 Jun 2017).Google ScholarGoogle Scholar
  9. Martin Haeusler, Thomas Trojer, Johannes Kessler, Matthias Farwick, Emmanuel Nowakowski, and Ruth Breu. 2018. Combining Versioning and Metamodel Evolution in the ChronoSphere Model Repository. In SOFSEM 2018: Theory and Practice of Computer Science. Springer International Publishing, Cham, 153--167.Google ScholarGoogle Scholar
  10. Thomas Hartmann, Francois FOUQUET, Matthieu Jimenez, Romain Rouvoy, and Yves Le Traon. 2017. Analyzing Complex Data in Motion at Scale with Temporal Graphs. In The 29th International Conference on Software Engineering & Knowledge Engineering (SEKE'17). KSI Research, Pittsburgh, United States, 6. https://hal.inria.fr/hal-01511636Google ScholarGoogle ScholarCross RefCross Ref
  11. Ábel Hegedüs, Ákos Horváth, István Ráth, Rodrigo Rizzi Starr, and Dániel Varró. 2016. Query-driven soft traceability links for models. Software & Systems Modeling 15, 3 (01 Jul 2016), 733--756. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. No Magic Inc. {n. d.}. MagicDraw. https://www.nomagic.com/products/magicdrawGoogle ScholarGoogle Scholar
  13. No Magic Inc. {n. d.}. Teamwork Cloud. https://www.nomagic.com/products/teamwork-cloudGoogle ScholarGoogle Scholar
  14. Michael Jackson. {n. d.}. Universal Javascript. Retrieved July 19, 2018 from https://cdb.reacttraining.com/universal-javascript-4761051b7ae9Google ScholarGoogle Scholar
  15. IncQuery Labs. {n. d.}. IncQuery for MagicDraw. https://incquerylabs.com/incqueryGoogle ScholarGoogle Scholar
  16. IncQuery Labs. 2017. The MagicDraw VIATRA Query performance benchmark. https://github.com/IncQueryLabs/magicdraw-viatra-benchmark.Google ScholarGoogle Scholar
  17. Intercax LLC. {n. d.}. Syndeia. http://intercax.com/products/syndeiaGoogle ScholarGoogle Scholar
  18. MBEE {n. d.}. http://www.openmbee.org/.Google ScholarGoogle Scholar
  19. Modeliosoft. {n. d.}. Modelio Constellation. https://www.modeliosoft.com/en/products/modelio-constellation.htmlGoogle ScholarGoogle Scholar
  20. Obeo. {n. d.}. Obeo Designer Team. https://www.obeodesigner.com/en/collaborative-features.Google ScholarGoogle Scholar
  21. The VIATRA Project. {n. d.}. V4MD. https://github.com/viatra/v4mdGoogle ScholarGoogle Scholar
  22. Gábor Szárnyas, Benedek Izsó, István Ráth, and Dániel Varró. 2017. The Train Benchmark: cross-technology performance evaluation of continuous model queries. Software & Systems Modeling (17 Jan 2017).Google ScholarGoogle Scholar
  23. The Apache Foundation. {n. d.}. Cassandra. http://cassandra.apache.orgGoogle ScholarGoogle Scholar
  24. The Eclipse Foundation. {n. d.}. CDO. http://www.eclipse.org/cdo.Google ScholarGoogle Scholar
  25. The Eclipse Foundation. {n. d.}. Eclipse Modeling Framework. http://www.eclipse.org/emf/.Google ScholarGoogle Scholar
  26. The Eclipse Foundation. {n. d.}. EMFStore. http://www.eclipse.org/emfstore.Google ScholarGoogle Scholar
  27. The Eclipse Foundation. {n. d.}. Vert.x. https://vertx.ioGoogle ScholarGoogle Scholar
  28. The MONDO Project. 2015. Deliverable 5.4: Heterogeneous Model Management Framework. https://tinyurl.com/mondo-d54Google ScholarGoogle Scholar
  29. Juha-Pekka Tolvanen. 2007. MetaEdit+: Domain-Specific Modeling and Product Generation Environment. In Software Product Lines, 11th Int. Conf. SPLC 2007, Kyoto, Japan. 145--146.Google ScholarGoogle Scholar
  30. Dániel Varró, Gábor Bergmann, Ábel Hegedüs, Ákos Horváth, István Ráth, and Zoltán Ujhelyi. 2016. Road to a reactive and incremental model transformation platform: three generations of the VIATRA framework. Software & Systems Modeling 15, 3 (01 Jul 2016), 609--629. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Jon Whittle, John Hutchinson, and Mark Rouncefield. 2014. The State of Practice in Model-Driven Engineering. IEEE Software 31, 3 (2014), 79--95.Google ScholarGoogle ScholarCross RefCross Ref

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      cover image ACM Conferences
      MODELS '18: Proceedings of the 21st ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
      October 2018
      214 pages

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      Publication History

      • Published: 14 October 2018

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      MODELS '18 Paper Acceptance Rate19of29submissions,66%Overall Acceptance Rate118of382submissions,31%

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