Change mining in adaptive process management systems

  • Authors:

  • Christian W. Günther;Stefanie Rinderle;Manfred Reichert;Wil van der Aalst

  • Affiliations:

  • Eindhoven University of Technology, The Netherlands;University of Ulm, Germany;University of Twente, The Netherlands;Eindhoven University of Technology, The Netherlands

  • Venue:
  • ODBASE'06/OTM'06 Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part I
  • Year:
  • 2006

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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.