Abstract
Process mining analyzes events that are logged during the execution of a business process. The level of abstraction at which a process model is recorded is reflected in the level of granularity of the data in the event log. When process activities are recorded as sensors readings, typically, they are very fined-grained and therefore difficult to interpret. To increase the understandability of the process model, events need to be abstracted into higher-level activities. This paper proposes vAMoS, a trace-based approach for event abstraction, which focuses on the identification of motifs on the traces, allowing some level of flexibility. The objective is the identification of recurring motifs on the traces in the event log. The presented algorithm uses a distance function to deal with the variability in the execution of activities. The result is a set of readable and interpretable motifs.
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Notes
- 1.
The source code can be found at https://dx.doi.org/10.5281/zenodo.6378497.
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Di Federico, G., Burattin, A. (2023). vAMoS: eVent Abstraction via Motifs Search. In: Cabanillas, C., Garmann-Johnsen, N.F., Koschmider, A. (eds) Business Process Management Workshops. BPM 2022. Lecture Notes in Business Information Processing, vol 460. Springer, Cham. https://doi.org/10.1007/978-3-031-25383-6_9
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