Supporting Dynamic, People-Driven Processes through Self-learning of Message Flows

  • Christoph Dorn
  • Schahram Dustdar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6741)


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.


message prediction process log mining people-driven processes process evolution message activity dependencies 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christoph Dorn
    • 1
    • 2
  • Schahram Dustdar
    • 2
  1. 1.Institute for Software ResearchUniversity of CaliforniaIrvineUSA
  2. 2.Distributed Systems GroupVienna University of TechnologyViennaAustria

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