Gradual Drift Detection in Process Models Using Conformance Metrics

TL;DR

This paper introduces an algorithm for automatically detecting and classifying gradual process model drifts using conformance metrics, outperforming existing methods in accuracy and delay.

Contribution

It presents a novel approach that detects and classifies gradual drifts in process models using conformance metrics, addressing a gap in existing sudden change detection methods.

Findings

Better detection accuracy than state-of-the-art algorithms.

Improved classification of gradual versus sudden changes.

Reduced detection delay and overlap in change regions.

Abstract

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.