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Discovering Hierarchical Consolidated Models from Process Families

  • Nour Assy
  • Boudewijn F. van Dongen
  • Wil M. P. van der Aalst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10253)

Abstract

Process families consist of different related variants that represent the same process. This might include, for example, processes executed similarly by different organizations or different versions of a same process with varying features. Motivated by the need to manage variability in process families, recent advances in process mining make it possible to discover, from a collection of event logs, a generic process model that explicitly describes the commonalities and differences across variants. However, existing approaches often result in flat complex models where it is hard to obtain a comparative insight into the common and different parts, especially when the family consists of a large number of process variants. This paper presents a decomposition-driven approach to discover hierarchical consolidated process models from collections of event logs. The discovered hierarchy consists of nested process fragments and allows to browse the variability at different levels of abstraction. The approach has been implemented as a plugin in ProM and was evaluated using synthetic and real-life event logs.

Keywords

Process mining Consolidated process families Hierarchical configurable models Decomposed discovery Configurable fragments 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nour Assy
    • 1
  • Boudewijn F. van Dongen
    • 1
  • Wil M. P. van der Aalst
    • 1
  1. 1.Department of Mathematics and Computer ScienceEindhoven University of TechnologyEindhovenThe Netherlands

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