On the discovery of process models from their instances ?
A thorough understanding of the way in which existing business processes currently practice is essential from the perspectives of both process reengineering and workflow management. In this paper, we present a framework and algorithms that derive the underlying process model from past executions. The process model employs a directed graph for representing the control dependencies among activities and associates a Boolean function on each edge to indicate the condition under which the edge is to be enabled. By modeling the execution of an activity as an interval, we have developed an algorithm that derives the directed graph in a faster, more accurate manner. This algorithm is further enhanced with a noise handling mechanism to tolerate noise, which frequently occur in the real world. Experimental results show that the proposed algorithm outperforms the existing ones in terms of efficiency and quality.
workflow management process discovery business process reengineering data mining