Process Discovery on Deviant Traces and Other Stranger Things

Abstract

As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative. Orthogonally to this classification, the vast majority of works envisage the discovery task as a one-class supervised learning process guided by the traces that are recorded into an input log. In this work instead, we focus on declarative processes and embrace the less-popular view of process discovery as a binary supervised learning task, where the input log reports both examples of the normal system execution, and traces representing a “stranger” behaviour according to the domain semantics. We therefore deepen how the valuable information brought by both these two sets can be extracted and formalised into a model that is “optimal” according to user-defined goals. Our approach, namely <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NegDis</monospace> , is evaluated w.r.t. other relevant works in this field, and shows promising results regarding both the performance and the quality of the obtained solution.