A Conformance Checking-based Approach for Drift Detection in Business Processes
This addresses drift detection for organizations using process mining, but it is incremental as it builds on existing conformance checking methods.
The paper tackles the problem of detecting concept drift in business processes, presenting C2D2, a conformance checking-based approach that improves accuracy with minimal delay, as validated on a synthetic dataset of 68 logs.
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.