# A Survey on Concept Drift in Process Mining

arXiv: https://arxiv.org/abs/2112.02000 · HTML: https://mlsanity.com/paper/2112.02000/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 94 references that are in the map.
- First posted on arXiv 3 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [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)
- [Event Stream-Based Process Discovery using Abstract Representations](https://mlsanity.com/paper/1704.08101/) (2017)

## Built on by (most cited first, 12 of 12)

- [Out-of-Distribution Generalization in Time Series: A Survey](https://mlsanity.com/paper/2503.13868/) (2025)
- [Process Variant Analysis Across Continuous Features: A Novel Framework](https://mlsanity.com/paper/2406.04347/) (2024)
- [Resilience to the Flowing Unknown: an Open Set Recognition Framework for Data Streams](https://mlsanity.com/paper/2411.00876/) (2024)
- [Interpretable Model Drift Detection](https://mlsanity.com/paper/2503.06606/) (2025)
- [Gradual Drift Detection in Process Models Using Conformance Metrics](https://mlsanity.com/paper/2207.11007/) (2022)
- [On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence](https://mlsanity.com/paper/2303.07940/) (2023)
- [Data is Moody: Discovering Data Modification Rules from Process Event Logs](https://mlsanity.com/paper/2312.14571/) (2023)
- [A Review of AI and Machine Learning Contribution in Predictive Business Process Management (Process Enhancement and Process Improvement Approaches)](https://mlsanity.com/paper/2407.11043/) (2024)
- [Online Discovery of Simulation Models for Evolving Business Processes (Extended Version)](https://mlsanity.com/paper/2506.10049/) (2025)
- [Discriminative Rule Learning for Outcome-Guided Process Model Discovery](https://mlsanity.com/paper/2510.27343/) (2025)
- [Autonomous Drift Learning in Data Streams: A Unified Perspective](https://mlsanity.com/paper/2605.01295/) (2026)
- [DT-GOL: Dual-Track Geometric Online Learning in Nonstationary Environment with Label Delay](https://mlsanity.com/paper/2606.22950/) (2026)

## Abstract (arXiv)

Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in process mining and bring forward a taxonomy of existing techniques for drift detection and online process mining for evolving environments. Existing works depict that (i) PM still primarily focuses on offline analysis, and (ii) the assessment of concept drift techniques in processes is cumbersome due to the lack of common evaluation protocol, datasets, and metrics.