Abstractions in Process Mining: A Taxonomy of Patterns

  • R. P. Jagadeesh Chandra Bose
  • Wil M. P. van der Aalst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5701)


Process mining refers to the extraction of process models from event logs. Real-life processes tend to be less structured and more flexible. Traditional process mining algorithms have problems dealing with such unstructured processes and generate spaghetti-like process models that are hard to comprehend. One reason for such a result can be attributed to constructing process models from raw traces without due pre-processing. In an event log, there can be instances where the system is subjected to similar execution patterns/behavior. Discovery of common patterns of invocation of activities in traces (beyond the immediate succession relation) can help in improving the discovery of process models and can assist in defining the conceptual relationship between the tasks/activities.

In this paper, we characterize and explore the manifestation of commonly used process model constructs in the event log and adopt pattern definitions that capture these manifestations, and propose a means to form abstractions over these patterns. We also propose an iterative method of transformation of traces which can be applied as a pre-processing step for most of today’s process mining techniques. The proposed approaches are shown to identify promising patterns and conceptually-valid abstractions on a real-life log. The patterns discussed in this paper have multiple applications such as trace clustering, fault diagnosis/anomaly detection besides being an enabler for hierarchical process discovery.


Maximal Element Edit Distance Tandem Array Abstract Entity Process Instance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • R. P. Jagadeesh Chandra Bose
    • 1
    • 2
  • Wil M. P. van der Aalst
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of TechnologyEindhovenThe Netherlands
  2. 2.Philips HealthcareBestThe Netherlands

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