A Recommender System for Process Discovery

Joel Ribeiro 1 Josep Carmona 1 Mustafa Mısır 2 Michele Sebag 3, 2
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.
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Joel Ribeiro, Josep Carmona, Mustafa Mısır, Michele Sebag. A Recommender System for Process Discovery. Business Process Management, Sep 2014, Eindhoven, Netherlands. pp.67 - 83, ⟨10.1007/978-3-319-10172-9_5⟩. ⟨hal-01109766⟩

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