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Comprehensive Process Drift Detection with Visual Analytics

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Book cover Conceptual Modeling (ER 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11788))

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Abstract

Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this paper, we propose a novel technique for managing process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The technique starts by clustering declarative process constraints discovered from recorded logs of executed business processes based on their similarity and then applies change point detection on the identified clusters to detect drifts. VDD complements these features with detailed visualizations and explanations of drifts. Our evaluation, both on synthetic and real-world logs, demonstrates all the aforementioned capabilities of the technique.

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Notes

  1. 1.

    https://doi.org/10.1007/s00607-015-0441-1.

  2. 2.

    https://doi.org/10.4121/uuid:0c60edf1-6f83-4e75-9367-4c63b3e9d5bb.

  3. 3.

    https://doi.org/10.4121/uuid:a7ce5c55-03a7-4583-b855-98b86e1a2b07.

  4. 4.

    https://doi.org/10.4121/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54 (preprocessed as in [18]).

  5. 5.

    https://github.com/cdc08x/MINERful.

  6. 6.

    https://github.com/deepcharles/ruptures.

  7. 7.

    https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html.

  8. 8.

    https://github.com/yesanton/Process-Drift-Visualization-With-Declare.

  9. 9.

    http://apromore.org/platform/tools.

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Acknowledgements

This work is partially funded by the EU H2020 program under MSCA-RISE agreement 645751 (RISE_BPM). Artem Polyvyanyy was partly supported by the Australian Research Council Discovery Project DP180102839.

Author information

Authors and Affiliations

  1. Vienna University of Economics and Business, Vienna, Austria

    Anton Yeshchenko, Claudio Di Ciccio & Jan Mendling

  2. The University of Melbourne, Parkville, VIC, 3010, Australia

    Artem Polyvyanyy

Authors
  1. Anton Yeshchenko
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  2. Claudio Di Ciccio
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  3. Jan Mendling
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  4. Artem Polyvyanyy
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Corresponding author

Correspondence to Anton Yeshchenko .

Editor information

Editors and Affiliations

  1. Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Alberto H. F. Laender

  2. Politecnico di Milano, Milan, Italy

    Barbara Pernici

  3. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  4. Univ Federal do Rio Grande do Sul, Porto Alegre, Brazil

    José Palazzo M. de Oliveira

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Yeshchenko, A., Di Ciccio, C., Mendling, J., Polyvyanyy, A. (2019). Comprehensive Process Drift Detection with Visual Analytics. In: Laender, A., Pernici, B., Lim, EP., de Oliveira, J. (eds) Conceptual Modeling. ER 2019. Lecture Notes in Computer Science(), vol 11788. Springer, Cham. https://doi.org/10.1007/978-3-030-33223-5_11

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  • DOI: https://doi.org/10.1007/978-3-030-33223-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33222-8

  • Online ISBN: 978-3-030-33223-5

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