DOI 10.1007/s10270-008-0106-z REGULAR PAPER Process mining: a two-step approach to balance between underfitting and overfitting

© The Author(s) 2008. This article is published with open access at Springerlink.com Abstract Process mining includes the automated discovery of processes 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 sub-set. 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 represen-tation language and very crude assumptions about complete-ness. As a result, parts of the model are “overfitting ” (allow only for what has actually been observed) while other parts Communicated by Prof. August-Wilhelm Scheer