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Business process deviance refers to the phenomenon whereby a subset of theexecutions of a business process deviate, in a negative or positive way, withrespect to {their} expected or desirable outcomes. Deviant executions of abusiness process include those that violate compliance rules, or executionsthat undershoot or exceed performance targets. Deviance mining is concernedwith uncovering the reasons for deviant executions by analyzing event logsstored by the systems supporting the execution of a business process. In thispaper, the problem of explaining deviations in business processes is firstinvestigated by using features based on sequential and declarative patterns,and a combination of them. Then, the explanations are further improved byleveraging the data attributes of events and traces in event logs throughfeatures based on pure data attribute values and data-aware declarative rules.The explanations characterizing the deviances are then extracted by direct andindirect methods for rule induction. Using real-life logs from multipledomains, a range of feature types and different forms of decision rules areevaluated in terms of their ability to accurately discriminate betweennon-deviant and deviant executions of a process as well as in terms ofunderstandability of the final outcome returned to the users.

Exploring Business Process Deviance with Sequential and Declarative Patterns