# Automated Discovery of Process Models from Event Logs: Review and Benchmark

arXiv: https://arxiv.org/abs/1705.02288 · HTML: https://mlsanity.com/paper/1705.02288/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2017.

## Built on by (most cited first, 28 of 28)

- [A systematic literature review on state-of-the-art deep learning methods for process prediction](https://mlsanity.com/paper/2101.09320/) (2021)
- [Automating the Enterprise with Foundation Models](https://mlsanity.com/paper/2405.03710/) (2024)
- [Process Discovery Using Graph Neural Networks](https://mlsanity.com/paper/2109.05835/) (2021)
- [Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Prediction](https://mlsanity.com/paper/2312.08847/) (2023)
- [Bridging Domain Knowledge and Process Discovery Using Large Language Models](https://mlsanity.com/paper/2408.17316/) (2024)
- [Pairing Conceptual Modeling with Machine Learning](https://mlsanity.com/paper/2106.14251/) (2021)
- [Process discovery on deviant traces and other stranger things](https://mlsanity.com/paper/2109.14883/) (2021)
- [Exploring Business Process Deviance with Sequential and Declarative Patterns](https://mlsanity.com/paper/2111.12454/) (2021)
- [Using Meta-learning to Recommend Process Discovery Methods](https://mlsanity.com/paper/2103.12874/) (2021)
- [Masking Neural Networks Using Reachability Graphs to Predict Process Events](https://mlsanity.com/paper/2108.00404/) (2021)
- [Timeline-based Process Discovery](https://mlsanity.com/paper/2401.04114/) (2023)
- [Think-on-Process: Dynamic Process Generation for Collaborative Development of Multi-Agent System](https://mlsanity.com/paper/2409.06568/) (2024)
- [Control-flow Reconstruction Attacks on Business Process Models](https://mlsanity.com/paper/2409.10986/) (2024)
- [Unlocking Non-Block-Structured Decisions: Inductive Mining with Choice Graphs](https://mlsanity.com/paper/2505.07052/) (2025)
- [Can LLM Agents Sustain Long-Horizon Organizational Dynamics?](https://mlsanity.com/paper/2606.01199/) (2026)
- [A Systematic Literature Review on Process-Aware Recommender Systems](https://mlsanity.com/paper/2103.16654/) (2021)
- [Improving Heuristic-based Process Discovery Methods by Detecting Optimal Dependency Graphs](https://mlsanity.com/paper/2203.10145/) (2022)
- [Generalization in Automated Process Discovery: A Framework based on Event Log Patterns](https://mlsanity.com/paper/2203.14079/) (2022)
- [Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning](https://mlsanity.com/paper/2211.16687/) (2022)
- [Comparing Ordering Strategies For Process Discovery Using Synthesis Rules](https://mlsanity.com/paper/2301.02182/) (2023)
- [Towards Knowledge-Centric Process Mining](https://mlsanity.com/paper/2301.10927/) (2023)
- [Discovering Hierarchical Process Models: an Approach Based on Events Clustering](https://mlsanity.com/paper/2303.13531/) (2023)
- [Process-To-Text: A Framework for the Quantitative Description of Processes in Natural Language](https://mlsanity.com/paper/2305.14044/) (2023)
- [Mining a Minimal Set of Behavioral Patterns using Incremental Evaluation](https://mlsanity.com/paper/2402.02921/) (2024)
- [The Impact of Event Data Partitioning on Privacy-aware Process Discovery](https://mlsanity.com/paper/2507.06008/) (2025)
- [SHAining on Process Mining: Explaining Event Log Characteristics Impact on Algorithms](https://mlsanity.com/paper/2509.08482/) (2025)
- [From Data Lifting to Continuous Risk Estimation: A Process-Aware Pipeline for Predictive Monitoring of Clinical Pathways](https://mlsanity.com/paper/2605.03895/) (2026)
- [Resolution limits for process comparison from event data](https://mlsanity.com/paper/2609.20489/) (2026)

## Abstract (arXiv)

Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.