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Change Point Detection and Dealing with Gradual and Multi-order Dynamics in Process Mining

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Perspectives in Business Informatics Research (BIR 2015)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 229))

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Abstract

In recent years process mining techniques have matured. Provided that the process is stable and enough example traces have been recorded in the event log, it is possible to discover a high-quality process model that can be used for performance analysis, compliance checking, and prediction. Unfortunately, most processes are not in steady-state and process discovery techniques have problems uncovering “second-order dynamics” (i.e., the process itself changes while being analyzed). This paper describes an approach to discover a variety of concept drifts in processes. Unlike earlier approaches, we can discover gradual drifts and multi-order dynamics (e.g., recurring seasonal effects mixed with the effects of an economic crisis). We use a novel adaptive windowing approach to robustly localize changes (gradual or sudden). Our extensive evaluation (based on objective criteria) shows that the new approach is able to efficiently uncover a broad range of drifts in processes.

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Notes

  1. 1.

    See www.processmining.org for more information and to download ProM.

  2. 2.

    A moving window is used to generate the series of populations.

  3. 3.

    The presence of troughs in the p-value plot indicates that the process was subjected to changes.

  4. 4.

    To simplify discussion, the duration for which a process variant is active is kept uniform. However, in reality, processes can be deployed for varying durations and can be changed at varying intervals.

  5. 5.

    This is incorporated in the adaptive windowing technique (the step size parameter) presented in this paper.

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Acknowledgments

J. Martjushev is grateful to Archimedes Foundation and the Ministry of Education and Research for funding his research through the national scholarship program, Kristjan Jaak.

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Authors and Affiliations

  1. University of Tartu, Tartu, Estonia

    J. Martjushev

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    J. Martjushev, R. P. Jagadeesh Chandra Bose & Wil M. P. van der Aalst

Authors
  1. J. Martjushev
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  2. R. P. Jagadeesh Chandra Bose
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  3. Wil M. P. van der Aalst
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Correspondence to R. P. Jagadeesh Chandra Bose .

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Editors and Affiliations

  1. University of Tartu, Tartu, Estonia

    Raimundas Matulevičius

  2. University of Tartu, Tartu, Estonia

    Marlon Dumas

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Martjushev, J., Bose, R.P.J.C., van der Aalst, W.M.P. (2015). Change Point Detection and Dealing with Gradual and Multi-order Dynamics in Process Mining. In: Matulevičius, R., Dumas, M. (eds) Perspectives in Business Informatics Research. BIR 2015. Lecture Notes in Business Information Processing, vol 229. Springer, Cham. https://doi.org/10.1007/978-3-319-21915-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-21915-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21914-1

  • Online ISBN: 978-3-319-21915-8

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