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As the need to understand and formalise business processes into a model hasgrown over the last years, the process discovery research field has gained moreand more importance, developing two different classes of approaches to modelrepresentation: procedural and declarative. Orthogonally to thisclassification, the vast majority of works envisage the discovery task as aone-class supervised learning process guided by the traces that are recordedinto an input log. In this work instead, we focus on declarative processes andembrace the less-popular view of process discovery as a binary supervisedlearning task, where the input log reports both examples of the normal systemexecution, and traces representing "stranger" behaviours according to thedomain semantics. We therefore deepen how the valuable information brought byboth these two sets can be extracted and formalised into a model that is"optimal" according to user-defined goals. Our approach, namely NegDis, isevaluated w.r.t. other relevant works in this field, and shows promisingresults as regards both the performance and the quality of the obtainedsolution.

Process discovery on deviant traces and other stranger things