  Real life business processes change over time, in both planned and unexpected
ways. The detection of these changes is crucial for organizations to ensure
that the expected and the real behavior are as similar as possible. These
changes over time are called concept drift and its detection is a big challenge
in process mining since the inherent complexity of the data makes difficult
distinguishing between a change and an anomalous execution. In this paper, we
present C2D2 (Conformance Checking-based Drift Detection), a new approach to
detect sudden control-flow changes in the process models from event traces.
C2D2 combines discovery techniques with conformance checking methods to perform
an offline detection. Our approach has been validated with a synthetic
benchmarking dataset formed by 68 logs, showing an improvement in the accuracy
while maintaining a minimum delay in the drift detection.
