Enhancing Declare Maps Based on Event Correlations

  • R. P. Jagadeesh Chandra Bose
  • Fabrizio Maria Maggi
  • Wil M. P. van der Aalst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8094)


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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • R. P. Jagadeesh Chandra Bose
    • 1
  • Fabrizio Maria Maggi
    • 2
  • Wil M. P. van der Aalst
    • 1
  1. 1.Eindhoven University of TechnologyThe Netherlands
  2. 2.University of TartuEstonia

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