# Timeline-based Process Discovery

arXiv: https://arxiv.org/abs/2401.04114 · HTML: https://mlsanity.com/paper/2401.04114/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science](https://mlsanity.com/paper/1905.06169/) (2019)
- [Automated Discovery of Process Models from Event Logs: Review and Benchmark](https://mlsanity.com/paper/1705.02288/) (2017)
- [EmoCo: Visual Analysis of Emotion Coherence in Presentation Videos](https://mlsanity.com/paper/1907.12918/) (2019)
- [Survey on Visual Analysis of Event Sequence Data](https://mlsanity.com/paper/2006.14291/) (2020)
- [DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways](https://mlsanity.com/paper/1904.11652/) (2019)
- [A Survey of Approaches for Event Sequence Analysis and Visualization using the ESeVis Framework](https://mlsanity.com/paper/2202.07941/) (2022)
- [In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals](https://mlsanity.com/paper/2008.09552/) (2020)

## Built on by (most cited first, 1 of 1)

- [Fairness in Healthcare Processes: A Quantitative Analysis of Decision Making in Triage](https://mlsanity.com/paper/2601.11065/) (2026)

## Abstract (arXiv)

A key concern of automatic process discovery is to provide insights into performance aspects of business processes. Waiting times are of particular importance in this context. For that reason, it is surprising that current techniques for automatic process discovery generate directly-follows graphs and comparable process models, but often miss the opportunity to explicitly represent the time axis. In this paper, we present an approach for automatically constructing process models that explicitly align with a time axis. We exemplify our approach for directly-follows graphs. Our evaluation using two BPIC datasets and a proprietary dataset highlight the benefits of this representation in comparison to standard layout techniques.