Service discovery from observed behavior while guaranteeing deadlock freedom in collaborations

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 --fi tness, 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.