2 Software Design and Management (sd&m AG),

Abstract. Process mining includes the automated discovery of pro-cesses from event logs. Based on observed events (e.g., activities being executed or messages being exchanged) a process model is constructed. One of the essential problems in process mining is that one cannot assume to have seen all possible behavior. At best, one has seen a representative subset. Therefore, classical synthesis techniques are not suitable as they aim at finding a model that is able to exactly reproduce the log. Existing process mining techniques try to avoid such “overfitting ” by generalizing the model to allow for more behavior. This generalization is often driven by the representation language and very crude assumptions about com-pleteness. As a result, parts of the model are “overfitting ” (allow only what has actually been observed) while other parts may be “underfit-ting ” (allow for much more behavior without strong support for it). None of the existing techniques enables the user to control the balance between “overfitting ” and “underfitting”. To address this, we propose a two-step approach. First, using a configurable approach, a transition system is constructed. Then, using the “theory of regions”, the model is synthe-sized. The approach has been implemented in the context of ProM and overcomes many of the limitations of traditional approaches.