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International Conference on Business Process Management

BPM 2013: Business Process Management Workshops pp 15–27Cite as

Discovering Stochastic Petri Nets with Arbitrary Delay Distributions from Event Logs

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

Abstract

Capturing the performance of a system or business process as accurately as possible is important, as models enriched with performance information provide valuable input for analysis, operational support, and prediction. Due to their computationally nice properties, memoryless models such as exponentially distributed stochastic Petri nets have earned much attention in research and industry. However, there are cases when the memoryless property is clearly not able to capture process behavior, e.g., when dealing with fixed time-outs.

We want to allow models to have generally distributed durations to be able to capture the behavior of the environment and resources as accurately as possible. For these more expressive process models, the execution policy has to be specified in more detail. In this paper, we present and evaluate process discovery algorithms for each of the execution policies. The introduced approach uses raw event execution data to discover various classes of stochastic Petri nets. The algorithms are based on the notion of alignments and have been implemented as a plug-in in the process mining framework ProM.

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Notes

  1. 1.

    See the StochasticPetriNet package of ProM (http://www.processmining.org).

  2. 2.

    See package logspline in R. (http://www.r-project.org/).

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Author information

Authors and Affiliations

  1. Business Process Technology Group, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany

    Andreas Rogge-Solti & Mathias Weske

  2. Department of Information Systems, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands

    Wil M. P. van der Aalst

Authors
  1. Andreas Rogge-Solti
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  2. Wil M. P. van der Aalst
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  3. Mathias Weske
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Corresponding author

Correspondence to Andreas Rogge-Solti .

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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Rogge-Solti, A., van der Aalst, W.M.P., Weske, M. (2014). Discovering Stochastic Petri Nets with Arbitrary Delay Distributions from Event Logs. 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_2

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  • DOI: https://doi.org/10.1007/978-3-319-06257-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06256-3

  • Online ISBN: 978-3-319-06257-0

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