Supporting dynamic, people-driven processes through self-learning of message flows

Authors:
Christoph Dorn;Schahram Dustdar
Affiliations:
Institute for Software Research, University of California, Irvine, CA and Distributed Systems Group, Vienna University of Technology, Vienna, Austria;Distributed Systems Group, Vienna University of Technology, Vienna, Austria
Venue:
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
Year:
2011

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Abstract

Flexibility and automatic learning are key aspects to support users in dynamic business environments such as value chains across SMEs or when organizing a large event. Process centric information systems need to adapt to changing environmental constraints as reflected in the user's behavior in order to provide suitable activity recommendations. This paper addresses the problem of automatically detecting and managing message flows in evolving people-driven processes. We introduce a probabilistic process model and message state model to learn message-activity dependencies, predict message occurrence, and keep the process model in line with real world user behavior. Our probabilistic process engine demonstrates rapid learning of message flow evolution while maintaining the quality of activity recommendations.