A Model-Driven Approach to Generate Relevant and Realistic Datasets

Adel Ferdjoukh 1 Eric Bourreau 2 Annie Chateau 3 Clémentine Nebut 1
1 MAREL - Models And Reuse Engineering, Languages
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 MAORE - Méthodes Algorithmes pour l'Ordonnancement et les Réseaux
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Disposing of relevant and realistic datasets is a difficult challenge in many areas, for benchmarking or testing purpose. Datasets may contain complexly structured data such as graphs or models, and obtaining such kind of data is sometimes expensive and available benchmarks are not as relevant as they should be. In this paper we propose a model-driven approach based on a probabilistic simulation using domain specific metrics for automated generation of relevant and realistic datasets.
Keywords : Relevant datasets generation Meta-model instantiation Probabilistic simulation
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Adel Ferdjoukh, Eric Bourreau, Annie Chateau, Clémentine Nebut. A Model-Driven Approach to Generate Relevant and Realistic Datasets. SEKE: Software Engineering and Knowledge Engineering, Jul 2016, Redwood City, San Francisco Bay, United States. pp.105-109, ⟨10.18293/SEKE2016-029⟩. ⟨lirmm-01397311⟩

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