# Comprehensive Process Drift Detection with Visual Analytics

arXiv: https://arxiv.org/abs/1907.06386 · HTML: https://mlsanity.com/paper/1907.06386/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [A Survey on Concept Drift in Process Mining](https://mlsanity.com/paper/2112.02000/) (2021)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [A Framework for Explainable Concept Drift Detection in Process Mining](https://mlsanity.com/paper/2105.13155/) (2021)
- [Generative Artificial Intelligence: Evolving Technology, Growing Societal Impact, and Opportunities for Information Systems Research](https://mlsanity.com/paper/2503.05770/) (2025)
- [Clustering Object-Centric Event Logs](https://mlsanity.com/paper/2207.12764/) (2022)
- [Process Model Forecasting Using Time Series Analysis of Event Sequence Data](https://mlsanity.com/paper/2105.01092/) (2021)
- [Gradual Drift Detection in Process Models Using Conformance Metrics](https://mlsanity.com/paper/2207.11007/) (2022)
- [Measuring Rule-based LTLf Process Specifications: A Probabilistic Data-driven Approach](https://mlsanity.com/paper/2305.05418/) (2023)

## Abstract (arXiv)

Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this paper, we propose a novel technique for managing process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The technique starts by clustering declarative process constraints discovered from recorded logs of executed business processes based on their similarity and then applies change point detection on the identified clusters to detect drifts. VDD complements these features with detailed visualizations and explanations of drifts. Our evaluation, both on synthetic and real-world logs, demonstrates all the aforementioned capabilities of the technique.