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International Conference on Business Process Management

BPM 2014: Business Process Management pp 234–249Cite as

Temporal Anomaly Detection in Business Processes

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 8659)

Abstract

The analysis of business processes is often challenging not only because of intricate dependencies between process activities but also because of various sources of faults within the activities. The automated detection of potential business process anomalies could immensely help business analysts and other process participants detect and understand the causes of process errors.

This work focuses on temporal anomalies, i.e., anomalies concerning the runtime of activities within a process. To detect such anomalies, we propose a Bayesian model that can be automatically inferred form the Petri net representation of a business process. Probabilistic inference on the above model allows the detection of non-obvious and interdependent temporal anomalies.

Keywords

  • outlier detection
  • documentation
  • statistical method
  • Bayesian networks

This work was partially supported by the European Union’s Seventh Framework Programme (FP7/2007-2013) grant 612052 (SERAMIS).

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  • DOI: 10.1007/978-3-319-10172-9_15
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Author information

Authors and Affiliations

  1. Vienna University of Economics and Business, Austria

    Andreas Rogge-Solti

  2. Hasso Plattner Institute, University of Potsdam, Germany

    Gjergji Kasneci

Authors
  1. Andreas Rogge-Solti
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  2. Gjergji Kasneci
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Editor information

Editors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, QLD, Australia

    Shazia Sadiq

  2. Information Systems Department, University of Haifa, Haifa, Israel

    Pnina Soffer

  3. IBM Research - Zurich, Rueschlikon, Switzerland

    Hagen Völzer

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Rogge-Solti, A., Kasneci, G. (2014). Temporal Anomaly Detection in Business Processes. In: Sadiq, S., Soffer, P., Völzer, H. (eds) Business Process Management. BPM 2014. Lecture Notes in Computer Science, vol 8659. Springer, Cham. https://doi.org/10.1007/978-3-319-10172-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-10172-9_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10171-2

  • Online ISBN: 978-3-319-10172-9

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