# Comprehensive Process Drift Detection with Visual Analytics

## TL;DR

This paper introduces Visual Drift Detection (VDD), a novel visual analytics technique for categorizing, drilling down, and quantifying process drifts in business process logs, enhancing process mining analysis.

## Contribution

The paper presents VDD, a new method combining clustering and change point detection with visualizations to improve process drift analysis in business logs.

## Key findings

- Effective drift detection on synthetic logs
- Successful application on real-world logs
- Enhanced understanding of process changes

## Abstract

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.