Gradual Drift Detection in Process Models Using Conformance Metrics
This addresses the need for organizations to optimize process performance by detecting gradual changes, but it is incremental as it builds on existing conformance checking methods for a specific type of change.
The paper tackles the problem of detecting gradual drifts in process models, which are often overlooked by existing algorithms focused on sudden changes, and proposes an algorithm using conformance metrics that achieves better detection and classification accuracy, delay, and change region overlapping than state-of-the-art methods on a synthetic dataset of 120 logs.
Changes, planned or unexpected, are common during the execution of real-life processes. Detecting these changes is a must for optimizing the performance of organizations running such processes. Most of the algorithms present in the state-of-the-art focus on the detection of sudden changes, leaving aside other types of changes. In this paper, we will focus on the automatic detection of gradual drifts, a special type of change, in which the cases of two models overlap during a period of time. The proposed algorithm relies on conformance checking metrics to carry out the automatic detection of the changes, performing also a fully automatic classification of these changes into sudden or gradual. The approach has been validated with a synthetic dataset consisting of 120 logs with different distributions of changes, getting better results in terms of detection and classification accuracy, delay and change region overlapping than the main state-of-the-art algorithms.