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Efficient Discovery of Compact Maximal Behavioral Patterns from Event Logs

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Date
2019
Dewey
Informatique générale
Sujet
Behavioral patterns; Process discovery; Pattern mining
DOI
http://dx.doi.org/10.1007/978-3-030-21290-2_36
Conference name
31st International Conference on Advanced Information Systems Engineering (CAiSE 2019)
Conference date
06-2019
Conference city
Rome
Conference country
Italy
Book title
Advanced Information Systems Engineering
Author
Giorgini, Paolo; Weber, Barbara
Publisher
Springer International Publishing
Publisher city
Berlin Heidelberg
Year
2019
Pages number
702
ISBN
978-3-030-21289-6
Book URL
10.1007/978-3-030-21290-2
URI
https://basepub.dauphine.fr/handle/123456789/19204
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Acheli, Mehdi
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Grigori, Daniela
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Weidlich, Matthias
253119 Humboldt-Universität zu Berlin
Type
Communication / Conférence
Item number of pages
579-594
Abstract (EN)
Techniques 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.

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