# Automated Discovery of Business Process Simulation Models from Event Logs

arXiv: https://arxiv.org/abs/1910.05404 · HTML: https://mlsanity.com/paper/1910.05404/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 17 of 17)

- [Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning](https://mlsanity.com/paper/2103.11944/) (2021)
- [AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation](https://mlsanity.com/paper/2408.08571/) (2024)
- [Can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models](https://mlsanity.com/paper/2303.17463/) (2023)
- [SimBank: from Simulation to Solution in Prescriptive Process Monitoring](https://mlsanity.com/paper/2506.14772/) (2025)
- [Enhancing Business Process Simulation Models with Extraneous Activity Delays](https://mlsanity.com/paper/2206.14051/) (2022)
- [Discovery and Simulation of Data-Aware Business Processes](https://mlsanity.com/paper/2408.13666/) (2024)
- [Control-flow Reconstruction Attacks on Business Process Models](https://mlsanity.com/paper/2409.10986/) (2024)
- [Generating the Traces You Need: A Conditional Generative Model for Process Mining Data](https://mlsanity.com/paper/2411.02131/) (2024)
- [Automated decision-making for dynamic task assignment at scale](https://mlsanity.com/paper/2504.19933/) (2025)
- [Extending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes](https://mlsanity.com/paper/2206.06111/) (2022)
- [CoSMo: a Framework to Instantiate Conditioned Process Simulation Models](https://mlsanity.com/paper/2303.17879/) (2023)
- [Business Process Simulation: Probabilistic Modeling of Intermittent Resource Availability and Multitasking Behavior](https://mlsanity.com/paper/2410.16941/) (2024)
- [Rethinking BPS: A Utility-Based Evaluation Framework](https://mlsanity.com/paper/2505.22316/) (2025)
- [A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation](https://mlsanity.com/paper/2505.22381/) (2025)
- [Online Discovery of Simulation Models for Evolving Business Processes (Extended Version)](https://mlsanity.com/paper/2506.10049/) (2025)
- [SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization](https://mlsanity.com/paper/2604.17351/) (2026)
- [Relational Learning as World Modeling](https://mlsanity.com/paper/2610.16763/) (2026)

## Abstract (arXiv)

Business process simulation is a versatile technique to estimate the performance of a process under multiple scenarios. This, in turn, allows analysts to compare alternative options to improve a business process. A common roadblock for business process simulation is that constructing accurate simulation models is cumbersome and error-prone. Modern information systems store detailed execution logs of the business processes they support. Previous work has shown that these logs can be used to discover simulation models. However, existing methods for log-based discovery of simulation models do not seek to optimize the accuracy of the resulting models. Instead they leave it to the user to manually tune the simulation model to achieve the desired level of accuracy. This article presents an accuracy-optimized method to discover business process simulation models from execution logs. The method decomposes the problem into a series of steps with associated configuration parameters. A hyper-parameter optimization method is used to search through the space of possible configurations so as to maximize the similarity between the behavior of the simulation model and the behavior observed in the log. The method has been implemented as a tool and evaluated using logs from different domains.