Formulating the Data-Flow Perspective for Business Process Management

Sherry X. Sun (), J. Leon Zhao (), Jay F. Nunamaker () and Olivia R. Liu Sheng ()
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Sherry X. Sun: Department of Management Information Systems, University of Arizona, Tucson, Arizona 85721
J. Leon Zhao: Department of Management Information Systems, University of Arizona, Tucson, Arizona 85721
Jay F. Nunamaker: Department of Management Information Systems, University of Arizona, Tucson, Arizona 85721
Olivia R. Liu Sheng: Accounting and Information Systems, University of Utah, Salt Lake City, Utah 84112

Information Systems Research, 2006, vol. 17, issue 4, 374-391

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.

Keywords: workflow modeling; data-flow specification; data-flow anomalies; data-flow verification; dependency analysis; process data diagram (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (10)

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