Glossary
- Conformance checking:
-
Monitoring deviations by comparing model and log
- Event log:
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Multiset of traces
- Event:
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Occurrence of some discrete incident (e.g., completion of an activity)
- Process discovery:
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Extracting process models from an event log
- Process mining:
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Collection of techniques to discover, monitor, and improve real processes by extracting knowledge from event data
- Trace:
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Sequence of events
Definition
Processes leave footprints in information systems just like people leave footprints in grassy spaces. Desire lines, i.e., the tracks formed by erosion showing where people really walk, may be very different from the formal pathways. When people deviate from the official path there is often a good reason and room for improvement. The goal of process mining is to extract desire lines from event logs, e.g., to automatically infer a process model from raw events recorded by some...
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Recommended Reading
To get started with process mining, the reader is advised to read the book “Process mining: data science in action” (van der Aalst 2016)
Acknowledgments
The author would like to thank all involved in the development of the process mining tool ProM and related techniques (processmining.org) and all members of the IEEE Task Force on Process Mining (www.win.tue.nl/ieeetfpm/).
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van der Aalst, W.M.P. (2018). Desire Lines in Big Data. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_396
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