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