A Conformance Checking-based Approach for Drift Detection in Business Processes

TL;DR

This paper introduces C2D2, a novel conformance checking-based method for detecting sudden process model changes in event logs, improving accuracy and detection delay in process mining.

Contribution

The paper presents a new approach combining discovery and conformance checking for offline drift detection in business processes, addressing challenges of complexity and anomaly differentiation.

Findings

Improved accuracy in drift detection.

Maintains minimal delay in detecting changes.

Validated on synthetic datasets with 68 logs.

Abstract

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.