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Efficient Discovery of Compact Maximal Behavioral Patterns from Event Logs

hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorAcheli, Mehdi
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorGrigori, Daniela
hal.structure.identifierHumboldt-Universität zu Berlin
dc.contributor.authorWeidlich, Matthias
dc.date.accessioned2019-07-09T09:50:52Z
dc.date.available2019-07-09T09:50:52Z
dc.date.issued2019
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19204
dc.language.isoenen
dc.subjectBehavioral patternsen
dc.subjectProcess discoveryen
dc.subjectPattern miningen
dc.subject.ddc004en
dc.titleEfficient Discovery of Compact Maximal Behavioral Patterns from Event Logsen
dc.typeCommunication / Conférence
dc.description.abstractenTechniques for process discovery support the analysis of information systems by constructing process models from event logs that are recorded during system execution. In recent years, various algorithms to discover end-to-end process models have been proposed. Yet, they do not cater for domains in which process execution is highly flexible, as the unstructuredness of the resulting models renders them meaningless. It has therefore been suggested to derive insights about flexible processes by mining behavioral patterns, i.e., models of frequently recurring episodes of a process’ behavior. However, existing algorithms to mine such patterns suffer from imprecision and redundancy of the mined patterns and a comparatively high computational effort. In this work, we overcome these limitations with a novel algorithm, coined COBPAM (COmbination based Behavioral Pattern Mining). It exploits a partial order on potential patterns to discover only those that are compact and maximal, i.e. least redundant. Moreover, COBPAM exploits that complex patterns can be characterized as combinations of simpler patterns, which enables pruning of the pattern search space. Efficiency is improved further by evaluating potential patterns solely on parts of an event log. Experiments with real-world data demonstrates how COBPAM improves over the state-of-the-art in behavioral pattern mining.en
dc.identifier.citationpages579-594en
dc.relation.ispartoftitleAdvanced Information Systems Engineeringen
dc.relation.ispartofeditorGiorgini, Paolo
dc.relation.ispartofeditorWeber, Barbara
dc.relation.ispartofpublnameSpringer International Publishingen
dc.relation.ispartofpublcityBerlin Heidelbergen
dc.relation.ispartofdate2019
dc.relation.ispartofpages702en
dc.relation.ispartofurl10.1007/978-3-030-21290-2en
dc.subject.ddclabelInformatique généraleen
dc.relation.ispartofisbn978-3-030-21289-6en
dc.relation.conftitle31st International Conference on Advanced Information Systems Engineering (CAiSE 2019)en
dc.relation.confdate2019-06
dc.relation.confcityRomeen
dc.relation.confcountryItalyen
dc.relation.forthcomingnonen
dc.identifier.doi10.1007/978-3-030-21290-2_36en
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2019-07-09T09:46:26Z
hal.identifierhal-02177689*
hal.version1*
hal.author.functionaut
hal.author.functionaut
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