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International Conference on Business Process Management

BPM 2013: Business Process Management Workshops pp 66–78Cite as

Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 171))

Abstract

Given an event log describing observed behaviour, process discovery aims to find a process model that ‘best’ describes this behaviour. A large variety of process discovery algorithms has been proposed. However, no existing algorithm returns a sound model in all cases (free of deadlocks and other anomalies), handles infrequent behaviour well and finishes quickly. We present a technique able to cope with infrequent behaviour and large event logs, while ensuring soundness. The technique has been implemented in ProM and we compare the technique with existing approaches in terms of quality and performance.

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Notes

  1. 1.

    A trace model allows for all traces in the event log, but no other behaviour.

  2. 2.

    We adapted the fitness computation in the PNetReplayer package to achieve this.

  3. 3.

    See http://www.win.tue.nl/coselog/wiki/start

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Authors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, the Netherlands

    Sander J. J. Leemans, Dirk Fahland & Wil M. P. van der Aalst

Authors
  1. Sander J. J. Leemans
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  2. Dirk Fahland
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  3. Wil M. P. van der Aalst
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Corresponding author

Correspondence to Sander J. J. Leemans .

Editor information

Editors and Affiliations

  1. University of Rostock, Rostock, Germany

    Niels Lohmann

  2. Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of (South Korea)

    Minseok Song

  3. Stockholm University, Stockholm, Sweden

    Petia Wohed

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Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P. (2014). Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds) Business Process Management Workshops. BPM 2013. Lecture Notes in Business Information Processing, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-319-06257-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-06257-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06256-3

  • Online ISBN: 978-3-319-06257-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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