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Gremlin-ATL: A Scalable Model Transformation Framework

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.
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Submitted on : Monday, September 18, 2017 - 5:01:52 PM
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Gwendal Daniel, Frédéric Jouault, Gerson Sunyé, Jordi Cabot. Gremlin-ATL: A Scalable Model Transformation Framework. ASE2017 : 32nd IEEE/ACM International Conference on Automated Software Engineering, Oct 2017, Urbana-Champaign, United States. ⟨10.1109/ASE.2017.8115658⟩. ⟨hal-01589582⟩



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