Business Process Deviance Mining: Review and Evaluation
This work addresses the problem of identifying reasons for deviations in business processes for practitioners, but it is incremental as it focuses on comparative evaluation of existing methods.
The paper systematically reviews and evaluates deviance mining approaches for business processes, finding that pattern mining features only slightly outperform simpler activity count features in discriminating between normal and deviant executions.
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 its 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 business process event logs. This article provides a systematic review and comparative evaluation of deviance mining approaches based on a family of data mining techniques known as sequence classification. Using real-life logs from multiple domains, we evaluate a range of feature types and classification methods in terms of their ability to accurately discriminate between normal and deviant executions of a process. We also analyze the interestingness of the rule sets extracted using different methods. We observe that feature sets extracted using pattern mining techniques only slightly outperform simpler feature sets based on counts of individual activity occurrences in a trace.