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Change Mining in Adaptive Process Management Systems

  • Christian W. Günther
  • Stefanie Rinderle
  • Manfred Reichert
  • Wil van der Aalst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4275)

Abstract

The wide-spread adoption of process-aware information systems has resulted in a bulk of computerized information about real-world processes. This data can be utilized for process performance analysis as well as for process improvement. In this context process mining offers promising perspectives. So far, existing mining techniques have been applied to operational processes, i.e., knowledge is extracted from execution logs (process discovery), or execution logs are compared with some a-priori process model (conformance checking). However, execution logs only constitute one kind of data gathered during process enactment. In particular, adaptive processes provide additional information about process changes (e.g., ad-hoc changes of single process instances) which can be used to enable organizational learning. In this paper we present an approach for mining change logs in adaptive process management systems. The change process discovered through process mining provides an aggregated overview of all changes that happened so far. This, in turn, can serve as basis for all kinds of process improvement actions, e.g., it may trigger process redesign or better control mechanisms.

Keywords

Process Mining Process Schema Label Transition System Process Instance Change Operation 
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 2006

Authors and Affiliations

  • Christian W. Günther
    • 1
  • Stefanie Rinderle
    • 2
  • Manfred Reichert
    • 3
  • Wil van der Aalst
    • 1
  1. 1.Eindhoven University of TechnologyThe Netherlands
  2. 2.University of UlmGermany
  3. 3.University of TwenteThe Netherlands

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