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Learning transformation rules from transformation examples: An approach based on Relational Concept Analysis

Abstract : In Model Driven Engineering (MDE), model transformations are basic and primordial entities, thus easing their design and implementation is an important issue. A quite recently proposed way to create model transformations consists in deducing a transformation from examples of transformed models. Examples are easier to write than a transformation program and are often already available. We propose in this paper a method based on a machine learning method of the lattice domain, the Relational Concept Analysis, and an implementation of this method.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00533375
Contributor : Marianne Huchard <>
Submitted on : Friday, November 5, 2010 - 6:30:27 PM
Last modification on : Friday, July 20, 2018 - 7:58:02 PM
Long-term archiving on: : Friday, October 26, 2012 - 3:02:44 PM

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Xavier Dolques, Marianne Huchard, Clémentine Nebut, Philippe Reitz. Learning transformation rules from transformation examples: An approach based on Relational Concept Analysis. EDOC: Enterprise Distributed Object Computing Conference, Oct 2010, Vittoria, Brazil. pp.27-32, ⟨10.1109/EDOCW.2010.32⟩. ⟨lirmm-00533375⟩

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