# Gradual Drift Detection in Process Models Using Conformance Metrics

arXiv: https://arxiv.org/abs/2207.11007 · HTML: https://mlsanity.com/paper/2207.11007/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 56 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [A Survey on Concept Drift in Process Mining](https://mlsanity.com/paper/2112.02000/) (2021)
- [Comprehensive Process Drift Detection with Visual Analytics](https://mlsanity.com/paper/1907.06386/) (2019)
- [Detecting sudden and gradual drifts in business processes from execution traces](https://mlsanity.com/paper/2005.04016/) (2020)
- [A Robust and Accurate Approach to Detect Process Drifts from Event Streams](https://mlsanity.com/paper/2103.10749/) (2021)
- [A Conformance Checking-based Approach for Drift Detection in Business Processes](https://mlsanity.com/paper/1907.04276/) (2019)

## Abstract (arXiv)

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.