Enhancing Declare maps based on event correlations

R.P. Jagadeesh Chandra Bose, F.M. Maggi, W.M.P. Aalst, van der

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

42 Citations (Scopus)


Traditionally, most process mining techniques aim at discovering procedural process models (e.g., Petri nets, BPMN, and EPCs) from event data. However, the variability present in less-structured flexible processes complicates the discovery of such procedural models. The "open world" assumption used by declarative models makes it easier to handle this variability. However, initial attempts to automatically discover declarative process models result in cluttered diagrams showing misleading constraints. Moreover, additional data attributes in event logs are not used to discover meaningful causalities. In this paper, we use correlations to prune constraints and to disambiguate event associations. As a result, the discovered process maps only show the more meaningful constraints. Moreover, the data attributes used for correlation and disambiguation are also used to find discriminatory patterns, identify outliers, and analyze bottlenecks (e.g., when do people violate constraints or miss deadlines). The approach has been implemented in ProM and experiments demonstrate the improved quality of process maps and diagnostics.
Original languageEnglish
Title of host publicationBusiness Process Management (11th International Conference, BPM 2013, Beijing, China, August 26-30, 2013. Proceedings)
EditorsF. Daniel, J. Wang, B. Weber
Place of PublicationBerlin
ISBN (Print)978-3-642-40175-6
Publication statusPublished - 2013

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


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