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Enhancing Event Log Quality: Detecting and Quantifying Timestamp Imperfections

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Business Process Management (BPM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12168))

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Abstract

Timestamp information recorded in event logs plays a crucial role in uncovering meaningful insights into business process performance and behaviour via Process Mining techniques. Inaccurate or incomplete timestamps may cause activities in a business process to be ordered incorrectly, leading to unrepresentative process models and incorrect process performance analysis results. Thus, the quality of timestamps in an event log should be evaluated thoroughly before the log is used as input for any Process Mining activity. To the best of our knowledge, research on the (automated) quality assessment of event logs remains scarce. Our work presents an automated approach for detecting and quantifying timestamp-related issues (timestamp imperfections) in an event log. We define 15 metrics related to timestamp quality across two axes: four levels of abstraction (event, activity, trace, log) and four quality dimensions (accuracy, completeness, consistency, uniqueness). We adopted the design science research paradigm and drew from knowledge related to data quality as well as event log quality. The approach has been implemented as a prototype within the open-source Process Mining framework ProM and evaluated using three real-life event logs and involving experts from practice. This approach paves the way for a systematic and interactive enhancement of timestamp imperfections during the data pre-processing phase of Process Mining projects.

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Acknowledgements

We would like to thank Queensland’s Motor Accident Insurance Commission and the Queensland University of Technology for allowing us access to their datasets.

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Authors and Affiliations

  1. FIM Research Center, Project Group Business and Information Systems Engineering of the Fraunhofer FIT, University of Bayreuth, Bayreuth, Germany

    Dominik Andreas Fischer, Christopher Gerhard Johannes van Dun & Maximilian Röglinger

  2. Queensland University of Technology, Brisbane, Australia

    Kanika Goel, Robert Andrews & Moe Thandar Wynn

Authors
  1. Dominik Andreas Fischer
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  2. Kanika Goel
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  3. Robert Andrews
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  4. Christopher Gerhard Johannes van Dun
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  5. Moe Thandar Wynn
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  6. Maximilian Röglinger
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Corresponding author

Correspondence to Dominik Andreas Fischer .

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Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Dirk Fahland

  2. FBK-irst, Trento, Italy

    Chiara Ghidini

  3. University of Münster, Münster, Germany

    Jörg Becker

  4. University of Tartu, Tartu, Estonia

    Marlon Dumas

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Fischer, D.A., Goel, K., Andrews, R., van Dun, C.G.J., Wynn, M.T., Röglinger, M. (2020). Enhancing Event Log Quality: Detecting and Quantifying Timestamp Imperfections. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds) Business Process Management. BPM 2020. Lecture Notes in Computer Science(), vol 12168. Springer, Cham. https://doi.org/10.1007/978-3-030-58666-9_18

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  • DOI: https://doi.org/10.1007/978-3-030-58666-9_18

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  • Online ISBN: 978-3-030-58666-9

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