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Stochastic Process Discovery by Weight Estimation

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Process Mining Workshops (ICPM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 406))

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Abstract

Many algorithms now exist for discovering process models from event logs. These models usually describe a control flow and are intended for use by people in analysing and improving real-world organizational processes. The relative likelihood of choices made while following a process (i.e., its stochastic behaviour) is highly relevant information which few existing algorithms make available in their automatically discovered models. This can be addressed by automatically discovered stochastic process models.

We introduce a framework for automatic discovery of stochastic process models, given a control-flow model and an event log. The framework introduces an estimator which takes a Petri net model and an event log as input, and outputs a Generalized Stochastic Petri net. We apply the framework, adding six new weight estimators, and a method for their evaluation. The algorithms have been implemented in the open-source process mining framework ProM. Using stochastic conformance measures, the resulting models have comparable conformance to existing approaches and are shown to be calculated more efficiently.

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Notes

  1. 1.

    Source code is accessible via https://github.com/adamburkegh/spd_we.

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Acknowledgement

Computational resources used included those provided by the eResearch Office at QUT.

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Authors and Affiliations

  1. Queensland University of Technology, Brisbane, Australia

    Adam Burke, Sander J. J. Leemans & Moe Thandar Wynn

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  1. Adam Burke
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  2. Sander J. J. Leemans
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  3. Moe Thandar Wynn
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Corresponding author

Correspondence to Adam Burke .

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Editors and Affiliations

  1. Queensland University of Technology, Brisbane, QLD, Australia

    Sander Leemans

  2. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

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Burke, A., Leemans, S.J.J., Wynn, M.T. (2021). Stochastic Process Discovery by Weight Estimation. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-72693-5_20

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  • DOI: https://doi.org/10.1007/978-3-030-72693-5_20

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