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Process mining is a set of techniques that are used by organizations tounderstand and improve their operational processes. The first essential step indesigning any process reengineering procedure is to find process improvementopportunities. In existing work, it is usually assumed that the set ofproblematic process instances in which an undesirable outcome occurs is knownprior or is easily detectable. So the process enhancement procedure involvesfinding the root causes and the treatments for the problem in those processinstances. For example, the set of problematic instances is considered as thosewith outlier values or with values smaller/bigger than a given threshold in oneof the process features. However, on various occasions, using this approach,many process enhancement opportunities, not captured by these problematicprocess instances, are missed. To overcome this issue, we formulate finding theprocess enhancement areas as a context-sensitive anomaly/outlier detectionproblem. We define a process enhancement area as a set of situations (processinstances or prefixes of process instances) where the process performance issurprising. We aim to characterize those situations where processperformance/outcome is significantly different from what was expectedconsidering its performance/outcome in similar situations. To evaluate thevalidity and relevance of the proposed approach, we have implemented andevaluated it on several real-life event logs.

Detecting Surprising Situations in Event Data