Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
VIATRA " Visual Automated Transformations for Formal Verification and Validation of UML Models
Proceedings of the 17th IEEE international conference on Automated software engineering
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Schema and ontology matching with COMA++
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Semi-automatic model integration using matching transformations and weaving models
Proceedings of the 2007 ACM symposium on Applied computing
Semi-automatic model integration using matching transformations and weaving models
Proceedings of the 2007 ACM symposium on Applied computing
Towards Model Transformation Generation By-Example
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
Relational concept discovery in structured datasets
Annals of Mathematics and Artificial Intelligence
Model Transformation as an Optimization Problem
MoDELS '08 Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems
Metamodel Matching for Automatic Model Transformation Generation
MoDELS '08 Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems
ICMT '09 Proceedings of the 2nd International Conference on Theory and Practice of Model Transformations
Model Transformation by Demonstration
MODELS '09 Proceedings of the 12th International Conference on Model Driven Engineering Languages and Systems
Model-to-model transformations by demonstration
ICMT'10 Proceedings of the Third international conference on Theory and practice of model transformations
Easing model transformation learning with automatically aligned examples
ECMFA'11 Proceedings of the 7th European conference on Modelling foundations and applications
Model transformation by example
MoDELS'06 Proceedings of the 9th international conference on Model Driven Engineering Languages and Systems
Lifting metamodels to ontologies: a step to the semantic integration of modeling languages
MoDELS'06 Proceedings of the 9th international conference on Model Driven Engineering Languages and Systems
Generating transformation definition from mapping specification: application to web service platform
CAiSE'05 Proceedings of the 17th international conference on Advanced Information Systems Engineering
MoDELS'05 Proceedings of the 2005 international conference on Satellite Events at the MoDELS
Weaving executability into object-oriented meta-languages
MoDELS'05 Proceedings of the 8th international conference on Model Driven Engineering Languages and Systems
Search-based model transformation by example
Software and Systems Modeling (SoSyM)
Generating model transformation rules from examples using an evolutionary algorithm
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
Relational concept analysis: mining concept lattices from multi-relational data
Annals of Mathematics and Artificial Intelligence
| Hi-index | 0.00 |
Model transformation by example (MTBE) aims at defining a model transformation according to a set of examples of this transformation. Examples are given in the form of pairs, each having an input model and its corresponding output transformed model, with the transformation traces. The transformation rules are then automatically extracted from the examples. In this paper, we propose a two-step approach to generate the transformation rules. In a first step, transformation patterns are learned from the examples through a classification of the model elements of the examples, and a classification of the transformation links using Formal Concept Analysis. In a second step, those transformation patterns are analyzed in order to select the more pertinent ones and to transform them into operational transformation rules written for the Jess rule engine. The generated rules are then executed on examples to evaluate their relevance through classical precision/recall measures.