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Process discovery techniques can be used to derive a process model from observed example behavior i.e., an event log. As the observed behavior is inherently incomplete and models may serve different purposes, four competing quality dimensions--fitness, precision, simplicity, and generalization--have to be balanced to produce a process model of high quality.  In this paper, we investigate the discovery of processes that are specified as services. Given a service S and observed behavior of a service P interacting with S, we discover a service model of P. Our algorithm balances the four quality dimensions based on user preferences. Moreover, unlike existing discovery approaches, we guarantees that the composition of S and P is deadlock free. The service discovery technique has been implemented in ProM and experiments using service models of industrial size demonstrate the scalability or our approach.

Service Discovery from Observed Behavior while Guaranteeing Deadlock Freedom in Collaborations