Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2207.11007 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- 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.