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Scalable model views over heterogeneous modeling technologies and resources

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Publication details

JournalSoftware and Systems Modeling
DateAccepted/In press - 23 Mar 2020
DateE-pub ahead of print - 8 Apr 2020
DatePublished (current) - Jul 2020
Number of pages25
Pages (from-to)827–851
Early online date8/04/20
Original languageEnglish


When engineering complex systems, models are typically used to represent various systems aspects. These models are often heterogeneous in terms of modeling languages, provenance, number or scale. As a result, the information actually relevant to engineers is usually split into different kinds of interrelated models. To be useful in practice, these models need to be properly integrated to provide global views over the system. This has to be made possible even when those models are serialized or stored in different formats adapted to their respective nature and scalability needs. Model view approaches have been proposed to tackle this issue. They provide unification mechanisms to combine and query various different models in a transparent way. These views usually target specific engineering tasks such as system design, monitoring and evolution. In an industrial context, there can be many large-scale use cases where model views can be beneficial, in order to trace runtime and design-time data, for example. However, existing model view solutions are generally designed to work on top of one single modeling technology (even though model import/export capabilities are sometimes provided). Moreover, they mostly rely on in-memory constructs and low-level modeling APIs that have not been designed to scale in the context of large models stored in different kinds of data sources. This paper presents a general solution to efficiently support scalable model views over heterogeneous modeling resources possibly handled via different modeling technologies. To this intent, it describes our integration approach between a model view framework and various modeling technologies providing access to multiple types of modeling resources (e.g., in XML/XMI, CSV, databases). It also presents how queries on such model views can be executed efficiently by benefiting from the optimization of the different model technologies and underlying persistence backends. Our solution has been evaluated on a practical large-scale use case provided by the industry-driven European MegaM@Rt2 project that aims at implementing a runtime ↔ design time feedback loop. The corresponding EMF-based tooling support, modeling artifacts and reproducible benchmarks are all available online.

    Research areas

  • Database, Design time, Heterogeneity, Modeling, Persistence, Runtime, Scalability, Views

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