  Business processes are prone to unexpected changes, as process workers may
suddenly or gradually start executing a process differently in order to adjust
to changes in workload, season, or other external factors. Early detection of
business process changes enables managers to identify and act upon changes that
may otherwise affect process performance. Business process drift detection
refers to a family of methods to detect changes in a business process by
analyzing event logs extracted from the systems that support the execution of
the process. Existing methods for business process drift detection are based on
an explorative analysis of a potentially large feature space and in some cases
they require users to manually identify specific features that characterize the
drift. Depending on the explored feature space, these methods miss various
types of changes. Moreover, they are either designed to detect sudden drifts or
gradual drifts but not both. This paper proposes an automated and statistically
grounded method for detecting sudden and gradual business process drifts under
a unified framework. An empirical evaluation shows that the method detects
typical change patterns with significantly higher accuracy and lower detection
delay than existing methods, while accurately distinguishing between sudden and
gradual drifts.
