By the same authors

Efficiently Querying Large-Scale Heterogeneous Models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Full text download(s)

Links

Published copy (DOI)

Author(s)

Department/unit(s)

Publication details

Title of host publicationProceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
DateAccepted/In press - 22 Aug 2020
DatePublished (current) - 27 Oct 2020
PublisherAssociation for Computing Machinery (ACM)
Place of PublicationNew York, NY, USA
Original languageEnglish
ISBN (Print)9781450381352

Publication series

NameMODELS '20
PublisherAssociation for Computing Machinery

Abstract

With the increase in the complexity of software systems, the size and the complexity of underlying models also increases proportionally. In a low-code system, models can be stored in different backend technologies and can be represented in various formats. Tailored high-level query languages are used to query such heterogeneous models, but typically this has a significant impact on performance. Our main aim is to propose optimization strategies that can help to query large models in various formats efficiently. In this paper, we present an approach based on compile-time static analysis and specific query optimizers/translators to improve the performance of complex queries over large-scale heterogeneous models. The proposed approach aims to bring efficiency in terms of query execution time and memory footprint, when compared to the naive query execution for low-code platforms.

    Research areas

  • scalability, model-driven engineering, model querying, static analysis

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations