Data-aware process mining: discovering decisions in processes using alignments

Authors:
Massimiliano de Leoni;Wil M. P. van der Aalst
Affiliations:
Eindhoven University of Technology, Eindhoven, The Netherlands;Eindhoven University of Technology, Eindhoven, The Netherlands
Venue:
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Year:
2013

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Abstract

Process discovery, i.e., learning process models from event logs, has attracted the attention of researchers and practitioners. Today, there exists a wide variety of process mining techniques that are able to discover the control-flow of a process based on event data. These techniques are able to identify decision points, but do not analyze data flow to find rules explaining why individual cases take a particular path. Fortunately, recent advances in conformance checking can be used to align an event log with data and a process model with decision points. These alignments can be used to generate a well-defined classification problem per decision point. This way data flow and guards can be discovered and added to the process model.