Causal Nets: A Modeling Language Tailored towards Process Discovery

  • Wil van der Aalst
  • Arya Adriansyah
  • Boudewijn van Dongen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6901)

Abstract

Process discovery—discovering a process model from example behavior recorded in an event log—is one of the most challenging tasks in process mining. The primary reason is that conventional modeling languages (e.g., Petri nets, BPMN, EPCs, and ULM ADs) have difficulties representing the observed behavior properly and/or succinctly. Moreover, discovered process models tend to have deadlocks and livelocks. Therefore, we advocate a new representation more suitable for process discovery: causal nets. Causal nets are related to the representations used by several process discovery techniques (e.g., heuristic mining, fuzzy mining, and genetic mining). However, unlike existing approaches, we provide declarative semantics more suitable for process mining. To clarify these semantics and to illustrate the non-local nature of this new representation, we relate causal nets to Petri nets.

Keywords

Input Place Business Process Modeling Notation Valid Sequence Conformance Check Book Hotel 
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 2011

Authors and Affiliations

  • Wil van der Aalst
    • 1
  • Arya Adriansyah
    • 1
  • Boudewijn van Dongen
    • 1
  1. 1.Department of Mathematics and Computer ScienceTechnische Universiteit EindhovenThe Netherlands

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