Deviance-aware Discovery of High Quality Process Models

Despite performance-oriented process mining techniques have been successfully applied in numerous application contexts, when applied to processes featuring complex and heterogeneous behaviors, they hardly produce models with a satisfactory level of accuracy, generality, and readability. In particular, the presence of deviant (i.e. anomalous/exceptional) traces often lead to cumbersome models with misleading performance statistics. Noise/outlier filtering solutions allows to alleviate this problem, and to discover a better model for "normal" executions, but do not provide insight on the nature and impact of deviant ones. The process discovery approach proposed here tries to recognize and describe both a normal execution scenario and a number of deviant ones for the analyzed log, by inducing two different kinds of models: (i) a list of readable clustering rules defining the deviance scenarios; (ii) a performance model for each discovered deviance scenario, and a "distilled" one for the "nor- mal" cases that do not fall in any deviant scenario. Technically, these models are discovered by mainly exploiting a conceptual clustering method, that greedily tries to separate groups of traces that maximally deviate from the current normality model. Tests on real-life logs confirmed the validity of the approach, and its ability to both find good performance models and support the analysis of deviant process instances.