[Zurück]

C. Dorn, S. Dustdar:
"Supporting Dynamic, People-Driven Processes through Self-learning of Message Flows";
Vortrag: Advanced Information Systems Engineering 23rd International Conference (CAiSE 2011), London, United Kingdom; 20.06.2011 - 24.06.2011; in: "Advanced Information Systems Engineering Proceedings of the 23rd International Conference (CAiSE 2011)", H. Mouratidis, C. Rolland (Hrg.); Springer, LNCS 6741 (2011), ISBN: 978-3-642-21639-8; S. 657 - 671.

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.

http://dx.doi.org/10.1007/978-3-642-21640-4_48

Projektleitung Schahram Dustdar:
Commius