Learning declarative process models from positive and negative traces

In the recent years, the growing number of recorded events made the interest in process mining techniques expand. These techniques make it possible to learn the model of a process, to compare a recent event log with an existing model or to enhance the process model using the information extracted from the log. Most of the existing process mining algorithms only make use of positive examples of a business process in order to extract its model, however, negative ones can bring major benefits. In this work, a discovery algorithm, inspired by the one presented by Mooney in 1995, that takes advantage of both positive and negative sequences of actions is presented in two different versions that return a declarative model connected respectively in disjunctive and conjunctive logic formulas.