On enabling data-aware compliance checking of business process models

  • Authors:

  • David Knuplesch;Linh Thao Ly;Stefanie Rinderle-Ma;Holger Pfeifer;Peter Dadam

  • Affiliations:

  • Institute of Databases and Information Systems, Ulm University, Germany;Institute of Databases and Information Systems, Ulm University, Germany;Faculty of Computer Science, University of Vienna, Austria;Institute of Artificial Intelligence, Ulm University, Germany;Institute of Databases and Information Systems, Ulm University, Germany

  • Venue:
  • ER'10 Proceedings of the 29th international conference on Conceptual modeling
  • Year:
  • 2010

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Abstract

In the light of an increasing demand on business process compliance, the verification of process models against compliance rules has become essential in enterprise computing. To be broadly applicable compliance checking has to support data-aware compliance rules as well as to consider data conditions within a process model. Independently of the actual technique applied to accomplish compliance checking, data-awareness means that in addition to the control flow dimension, the data dimension has to be explored during compliance checking. However, naive exploration of the data dimension can lead to state explosion. We address this issue by introducing an abstraction approach in this paper. We show how state explosion can be avoided by conducting compliance checking for an abstract process model and abstract compliance rules. Our abstraction approach can serve as preprocessing step to the actual compliance checking and provides the basis for more efficient application of existing compliance checking algorithms.