Formulating the Data-Flow Perspective for Business Process Management

  • Authors:

  • Sherry X. Sun;J. Leon Zhao;Jay F. Nunamaker;Olivia R. Liu Sheng

  • Affiliations:

  • Department of Management Information Systems, University of Arizona, Tucson, Arizona 85721;Department of Management Information Systems, University of Arizona, Tucson, Arizona 85721;Department of Management Information Systems, University of Arizona, Tucson, Arizona 85721;Accounting and Information Systems, University of Utah, Salt Lake City, Utah 84112

  • Venue:
  • Information Systems Research
  • Year:
  • 2006

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Abstract

Workflow technology has become a standard solution for managing increasingly complex business processes. Successful business process management depends on effective workflow modeling and analysis. One of the important aspects of workflow analysis is the data-flow perspective because, given a syntactically correct process sequence, errors can still occur during workflow execution due to incorrect data-flow specifications. However, there have been only scant treatments of the data-flow perspective in the literature and no formal methodologies are available for systematically discovering data-flow errors in a workflow model. As an indication of this research gap, existing commercial workflow management systems do not provide tools for data-flow analysis at design time. In this paper, we provide a data-flow perspective for detecting data-flow anomalies such as missing data, redundant data, and potential data conflicts. Our data-flow framework includes two basic components: data-flow specification and data-flow analysis; these components add more analytical rigor to business process management.