Formulating the data-flow perspective for business process management

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.