  Business process deviance refers to the phenomenon whereby a subset of the
executions of a business process deviate, in a negative or positive way, with
respect to {their} expected or desirable outcomes. Deviant executions of a
business process include those that violate compliance rules, or executions
that undershoot or exceed performance targets. Deviance mining is concerned
with uncovering the reasons for deviant executions by analyzing event logs
stored by the systems supporting the execution of a business process. In this
paper, the problem of explaining deviations in business processes is first
investigated by using features based on sequential and declarative patterns,
and a combination of them. Then, the explanations are further improved by
leveraging the data attributes of events and traces in event logs through
features based on pure data attribute values and data-aware declarative rules.
The explanations characterizing the deviances are then extracted by direct and
indirect methods for rule induction. Using real-life logs from multiple
domains, a range of feature types and different forms of decision rules are
evaluated in terms of their ability to accurately discriminate between
non-deviant and deviant executions of a process as well as in terms of
understandability of the final outcome returned to the users.
