# Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models

arXiv: https://arxiv.org/abs/1704.03520 · HTML: https://mlsanity.com/paper/1704.03520/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2017.

## Built on by (most cited first, 5 of 5)

- [Grouping Local Process Models](https://mlsanity.com/paper/2311.03040/) (2023)
- [Interactive Multi Interest Process Pattern Discovery](https://mlsanity.com/paper/2308.14475/) (2023)
- [Event Abstraction for Enterprise Collaboration Systems to Support Social Process Mining](https://mlsanity.com/paper/2308.04396/) (2023)
- [Combining Abstract Argumentation and Machine Learning for Efficiently Analyzing Low-Level Process Event Streams](https://mlsanity.com/paper/2505.05880/) (2025)
- [Framework for Grouping Local Process Models](https://mlsanity.com/paper/2607.04856/) (2026)

## Abstract (arXiv)

Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of granularity, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and then use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs.