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International Conference on Applications and Theory of Petri Nets and Concurrency

PETRI NETS 2015: Application and Theory of Petri Nets and Concurrency pp 287–308Cite as

Process Discovery Using Localized Events

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9115))

Abstract

Process mining techniques aim to analyze and improve conformance and performance of processes using event data. Process discovery is the most prominent process-mining task: A process model is derived based on an event log. The process model should be able to capture causalities, choices, concurrency, and loops. Process discovery is very challenging because of trade-offs between fitness, simplicity, precision, and generalization. Note that event logs typically only hold example behavior and cannot be assumed to be complete (to avoid overfitting). Dozens of process discovery techniques have been proposed. These use a wide range of approaches, e.g., language- or state-based regions, genetic mining, heuristics, expectation maximization, iterative log-splitting, etc. When models or logs become too large for analysis, the event log may be automatically decomposed or traces may be clustered before discovery. Clustering and decomposition are done automatically, i.e., no additional information is used. This paper proposes a different approach where a localized event log is assumed. Events are localized by assigning a non-empty set of regions to each event. It is assumed that regions can only interact through shared events. Consider for example the mining of software systems. The events recorded typically explicitly refer to parts of the system (components, services, etc.). Currently, such information is ignored during discovery. However, references to system parts may be used to localize events. Also in other application domains, it is possible to localize events, e.g., communication events in an organization may refer to multiple departments (that may be seen as regions). This paper proposes a generic process discovery approach based on localized event logs. The approach has been implemented in ProM and experimental results show that location information indeed helps to improve the quality of the discovered models.

This work is supported by the Basic Research Program of the National Research University Higher School of Economics.

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Authors and Affiliations

  1. Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands

    Wil M. P. van der Aalst & Eric Verbeek

  2. National Research University Higher School of Economics, Moscow, 101000, Russia

    Wil M. P. van der Aalst, Anna Kalenkova & Vladimir Rubin

Authors
  1. Wil M. P. van der Aalst
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  2. Anna Kalenkova
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  3. Vladimir Rubin
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  4. Eric Verbeek
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Corresponding author

Correspondence to Wil M. P. van der Aalst .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Raymond Devillers

  2. Tampere University of Technology, Tampere, Finland

    Antti Valmari

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van der Aalst, W.M.P., Kalenkova, A., Rubin, V., Verbeek, E. (2015). Process Discovery Using Localized Events. In: Devillers, R., Valmari, A. (eds) Application and Theory of Petri Nets and Concurrency. PETRI NETS 2015. Lecture Notes in Computer Science(), vol 9115. Springer, Cham. https://doi.org/10.1007/978-3-319-19488-2_15

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  • DOI: https://doi.org/10.1007/978-3-319-19488-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19487-5

  • Online ISBN: 978-3-319-19488-2

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