Finding Suitable Activity Clusters for Decomposed Process Discovery

Abstract : Event data can be found in any information system and provide the starting point for a range of process mining techniques. The widespread availability of large amounts of event data also creates new challenges. Existing process mining techniques are often unable to handle “big event data” adequately. Decomposed process mining aims to solve this problem by decomposing the process mining problem into many smaller problems which can be solved in less time, using less resources, or even in parallel. Many decomposed process mining techniques have been proposed in literature. Analysis shows that even though the decomposition step takes a relatively small amount of time, it is of key importance in finding a high-quality process model and for the computation time required to discover the individual parts. Currently there is no way to assess the quality of a decomposition beforehand. We define three quality notions that can be used to assess a decomposition, before using it to discover a model or check conformance with. We then propose a decomposition approach that uses these notions and is able to find a high-quality decomposition in little time.
Type de document :
Communication dans un congrès
Paolo Ceravolo; Barbara Russo; Rafael Accorsi. 4th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Nov 2014, Milan, Italy. Lecture Notes in Business Information Processing, LNBIP-237, pp.32-57, 2015, Data-Driven Process Discovery and Analysis. 〈10.1007/978-3-319-27243-6_2〉
Liste complète des métadonnées

https://hal.inria.fr/hal-01442339
Contributeur : Hal Ifip <>
Soumis le : vendredi 20 janvier 2017 - 15:39:14
Dernière modification le : vendredi 20 janvier 2017 - 15:41:56
Document(s) archivé(s) le : vendredi 21 avril 2017 - 15:46:10

Fichier

393788_1_En_2_Chapter.pdf
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

B. Hompes, H. Verbeek, W. Aalst. Finding Suitable Activity Clusters for Decomposed Process Discovery. Paolo Ceravolo; Barbara Russo; Rafael Accorsi. 4th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Nov 2014, Milan, Italy. Lecture Notes in Business Information Processing, LNBIP-237, pp.32-57, 2015, Data-Driven Process Discovery and Analysis. 〈10.1007/978-3-319-27243-6_2〉. 〈hal-01442339〉

Partager

Métriques

Consultations de la notice

86

Téléchargements de fichiers

31