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2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) (2017)
Urbana, IL, USA
Oct. 30, 2017 to Nov. 3, 2017
ISBN: 978-1-5386-3976-4
pp: 462-472
Gwendal Daniel , AtlanMod Team, Inria, IMT Atlantique, LS2N, Nantes, France
Frederic Jouault , TRAME Team, Groupe ESEO, Angers, France
Gerson Sunye , AtlanMod Team, Inria, IMT Atlantique, LS2N, Nantes, France
Jordi Cabot , ICREA, UOC, Barcelona, Spain
ABSTRACT
Industrial use of Model Driven Engineering techniques has emphasized the need for efficiently store, access, and transform very large models. While scalable persistence frameworks, typically based on some kind of NoSQL database, have been proposed to solve the model storage issue, the same level of performance improvement has not been achieved for the model transformation problem. Existing model transformation tools (such as the well-known ATL) often require the input models to be loaded in memory prior to the start of the transformation and are not optimized to benefit from lazy-loading mechanisms, mainly due to their dependency on current low-level APIs offered by the most popular modeling frameworks nowadays. In this paper we present Gremlin-ATL, a scalable and efficient model-to-model transformation framework that translates ATL transformations into Gremlin, a query language supported by several NoSQL databases. With Gremlin-ATL, the transformation is computed within the database itself, bypassing the modeling framework limitations and improving its performance both in terms of execution time and memory consumption. Tool support is available online.
INDEX TERMS
Unified modeling language, Databases, Load modeling, Computational modeling, Transforms, Tools, Database languages
CITATION

G. Daniel, F. Jouault, G. Sunye and J. Cabot, "Gremlin-ATL: A scalable model transformation framework," 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), Urbana, IL, USA, 2017, pp. 462-472.
doi:10.1109/ASE.2017.8115658
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