Barmpis, Konstantinos and Kolovos, Dimitrios S. orcid.org/0000-0002-1724-6563 (2014) Towards scalable querying of large-scale models. In: Modelling Foundations and Applications - 10th European Conference, ECMFA 2014, Held as Part of STAF 2014, Proceedings. 10th European Conference on Modelling Foundations and Applications, ECMFA 2014, Held as Part of Software Technologies: Applications and Foundations, STAF 2014, 21-25 Jul 2014 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer , GBR , pp. 35-50.
Abstract
Hawk is a modular and scalable framework that supports monitoring and indexing large collections of models stored in diverse version control repositories. Due to the aggregate size of indexed models, providing a reliable, usable, and fast mechanism for querying Hawk's index is essential. This paper presents the integration of Hawk with an existing model querying language, discusses the efficiency challenges faced, and presents an approach based on the use of derived features and indexes as a means of improving the performance of particular classes of queries. The paper also reports on the evaluation of a prototype that implements the proposed approach against the Grabats benchmark query, focusing on the observed efficiency benefits in terms of query execution time. It also compares the size and resource use of the model index against one created without using such optimizations.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Keywords: | model querying,model-driven engineering,Scalability |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 24 Apr 2024 13:10 |
Last Modified: | 05 Feb 2025 00:03 |
Published Version: | https://doi.org/10.1007/978-3-319-09195-2_3 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Identification Number: | 10.1007/978-3-319-09195-2_3 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:211827 |
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