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Online Anomaly Detection Using Statistical Leverage for Streaming Business Process Events

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Process Mining Workshops (ICPM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 406))

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Abstract

While several techniques for detecting trace-level anomalies in event logs in offline settings have appeared recently in the literature, such techniques are currently lacking for online settings. Event log anomaly detection in online settings can be crucial for discovering anomalies in process execution as soon as they occur and, consequently, allowing to promptly take early corrective actions. This paper describes a novel approach to event log anomaly detection on event streams that uses statistical leverage. Leverage has been used extensively in statistics to develop measures to identify outliers and it has been adapted in this paper to the specific scenario of event stream data. The proposed approach has been evaluated on both artificial and real event streams.

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Notes

  1. 1.

    These event logs belong to the ones made available by the Business Process Intelligence Challenge in 2012, 2013 and 2017.

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Authors and Affiliations

  1. Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea

    Jonghyeon Ko & Marco Comuzzi

Authors
  1. Jonghyeon Ko
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  2. Marco Comuzzi
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Corresponding author

Correspondence to Marco Comuzzi .

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Editors and Affiliations

  1. Queensland University of Technology, Brisbane, QLD, Australia

    Sander Leemans

  2. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

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Ko, J., Comuzzi, M. (2021). Online Anomaly Detection Using Statistical Leverage for Streaming Business Process Events. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-72693-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-72693-5_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72692-8

  • Online ISBN: 978-3-030-72693-5

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