A context driven approach for workflow mining

Authors:
Fusun Yaman;Tim Oates;Mark Burstein
Affiliations:
BBN Technologies, Cambridge, MA;Department of Computer Science and Electrical Engineering, UMBC, Baltimore, MD;BBN Technologies, Cambridge, MA
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

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Abstract

Existing work on workflow mining ignores the dataflow aspect of the problem. This is not acceptable for service-oriented applications that use Web services with typed inputs and outputs. We propose a novel algorithm WIT (Workflow Inference from Traces) which identifies the context similarities of the observed actions based on the dataflow and uses model merging techniques to generalize the control flow and the dataflow simultaneously. We identify the class of workflows that WIT can learn correctly. We implemented WIT and tested it on a real world medical scheduling domain where WIT was able to find a good approximation of the target workflow.