  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 "stranger" behaviours 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 NegDis, is
evaluated w.r.t. other relevant works in this field, and shows promising
results as regards both the performance and the quality of the obtained
solution.
