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A Systematic Review of Anomaly Detection for Business Process Event Logs

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Abstract

While a business process is most often executed following a normal path, anomalies may sometimes arise and can be captured in event logs. Event log anomalies stem, for instance, from system malfunctioning or unexpected behavior of human resources involved in a process. To identify and possibly fix these, anomaly detection has emerged recently as a key discipline in process mining. In the paper, the authors present a systematic review of the literature on business process event log anomaly detection. The review aims at selecting systematically studies in the literature that have tackled the issue of event log anomaly detection, classifying existing approaches based on criteria emerging from previous literature reviews, and identifying those research directions in this field that have not been explored extensively. Based on the results of the review, the authors argue that future research should look more specifically into anomaly detection on event streams, extending the number of event log attributes considered to determine anomalies, and producing more standard labeled datasets to benchmark the techniques proposed.

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Notes

  1. The BPIC event logs are available at

    (i) BPIC2012: https://doi.org/10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f,

    (ii) BPIC2013: https://doi.org/10.4121/uuid:a7ce5c55-03a7-4583-b855-98b86e1a2b07,

    (iii) BPIC2015: https://doi.org/10.4121/uuid:31a308ef-c844-48da-948c-305d167a0ec1,

    (iv) BPIC2017: https://doi.org/10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f,

    and (v) BPIC2018: https://doi.org/10.4121/uuid:3301445f-95e8-4ff0-98a4-901f1f204972.

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

  3. Available at https://doi.org/10.4121/uuid:76c46b83-c930-4798-a1c9-4be94dfeb741.

  4. Available at https://www.cs.unm.edu/~immsec/data/live-lpr.html.

  5. Available at https://www.bugzilla.org/.

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Authors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, 39100, Bolzano, Italy

    Jonghyeon Ko

  2. Department of Industrial Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea

    Marco Comuzzi

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  1. Jonghyeon Ko
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  2. Marco Comuzzi
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Correspondence to Marco Comuzzi.

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Accepted after 2 revision by Hajo Reijers.

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Ko, J., Comuzzi, M. A Systematic Review of Anomaly Detection for Business Process Event Logs. Bus Inf Syst Eng 65, 441–462 (2023). https://doi.org/10.1007/s12599-023-00794-y

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