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International Conference on Business Process Management

BPM 2018: Business Process Management pp 139–157Cite as

Unbiased, Fine-Grained Description of Processes Performance from Event Data

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11080)

Abstract

Performance is central to processes management and event data provides the most objective source for analyzing and improving performance. Current process mining techniques give only limited insights into performance by aggregating all event data for each process step. In this paper, we investigate process performance of all process behaviors without prior aggregation. We propose the performance spectrum as a simple model that maps all observed flows between two process steps together regarding their performance over time. Visualizing the performance spectrum of event logs reveals a large variety of very distinct patterns of process performance and performance variability that have not been described before. We provide a taxonomy for these patterns and a comprehensive overview of elementary and composite performance patterns observed on several real-life event logs from business processes and logistics. We report on a case study where performance patterns were central to identify systemic, but not globally visible process problems.

Keywords

  • Process mining
  • Performance analysis
  • Visual analytics

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  • DOI: 10.1007/978-3-319-98648-7_9
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Notes

  1. 1.

    https://doi.org/10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f5.

  2. 2.

    source code and further documentation available at https://github.com/processmining-in-logistics/psm.

  3. 3.

    available at https://data.4tu.nl/repository/collection:event_logs_real.

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Acknowledgements

The research leading to these results has received funding from Vanderlande Industries in the project “Process Mining in Logistics”. We thank Elena Belkina for support in the tool development.

Author information

Authors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Vadim Denisov, Dirk Fahland & Wil M. P. van der Aalst

  2. Department of Computer Science, RWTH Aachen, Aachen, Germany

    Wil M. P. van der Aalst

Authors
  1. Vadim Denisov
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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 Vadim Denisov .

Editor information

Editors and Affiliations

  1. Hasso-Plattner-Institute, University of Potsdam, Potsdam, Germany

    Prof. Dr. Mathias Weske

  2. Free University of Bozen Bolzano, Bolzano, Italy

    Dr. Marco Montali

  3. Data61, CSIRO, Eveleigh, NSW, Australia

    Dr. Ingo Weber

  4. University of Liechtenstein, Vaduz, Liechtenstein

    Prof. Jan vom Brocke

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Denisov, V., Fahland, D., van der Aalst, W.M.P. (2018). Unbiased, Fine-Grained Description of Processes Performance from Event Data. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds) Business Process Management. BPM 2018. Lecture Notes in Computer Science(), vol 11080. Springer, Cham. https://doi.org/10.1007/978-3-319-98648-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-98648-7_9

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