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