Abstract
Process mining techniques analyze processes based on event data. A crucial assumption for process analysis is that events correspond to occurrences of meaningful activities. Often, low-level events recorded by information systems do not directly correspond to these. Abstraction methods, which provide a mapping from the recorded events to activities recognizable by process workers, are needed. Existing supervised abstraction methods require a full model of the entire process as input and cannot handle noise. This paper proposes a supervised abstraction method based on behavioral activity patterns that capture domain knowledge on the relation between activities and events. Through an alignment between the activity patterns and the low-level event logs an abstracted event log is obtained. Events in the abstracted event log correspond to instantiations of recognizable activities. The method is evaluated with domain experts of a Norwegian hospital using an event log from their digital whiteboard system. The evaluation shows that state-of-the art process mining methods provide valuable insights on the usage of the system when using the abstracted event log, but fail when using the original lower level event log.
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Notes
- 1.
\( dom (f)\) denotes the domain of a function f.
- 2.
The used whiteboard system is Imatis Visi: http://www.imatis.com.
- 3.
Plug-in Log Abstraction of the ProM package LogEnhancement: http://promtools.org.
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Acknowledgments
We would like to thank Ivar Myrstad for his valuable insights on the digital whiteboard and his help with the case study.
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Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P., Toussaint, P.J. (2016). From Low-Level Events to Activities - A Pattern-Based Approach. In: La Rosa, M., Loos, P., Pastor, O. (eds) Business Process Management. BPM 2016. Lecture Notes in Computer Science(), vol 9850. Springer, Cham. https://doi.org/10.1007/978-3-319-45348-4_8
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