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

M. Leoni, de, W.M.P. Aalst, van der

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

159 Citations (Scopus)
7 Downloads (Pure)


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.
Original languageEnglish
Title of host publication28th Annual ACM Symposium on Applied Computing (Coimbra, Portugal, March 18-22, 2013)
Place of PublicationNew York NY
PublisherAssociation for Computing Machinery, Inc
ISBN (Print)978-1-4503-1656-9
Publication statusPublished - 2013
Event28th ACM Symposium on Applied Computing (SAC 2013) - Coimbra, Portugal
Duration: 18 Mar 201322 Mar 2013
Conference number: 28


Conference28th ACM Symposium on Applied Computing (SAC 2013)
Abbreviated titleSAC 2013


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