# Prosit: A tool for interactive and transparent process simulations

- Type: Conference paper
- URL: https://www.gyunam.com/publication/2025-icsoc-prosit/
- Date: 2025-12-01
- Authors: Francesco Vinci, Gyunam Park, Wil M. P. van der Aalst, Massimiliano de Leoni
- Venue: Proceedings of the 23rd International Conference on Service-Oriented Computing (ICSOC 2025), Demo Track. Lecture Notes in Computer Science, Springer, Cham
- PDF: https://www.gyunam.com/publication/2025-icsoc-prosit/paper.pdf
- Research lines: [Prescriptive process analytics and digital twins](https://www.gyunam.com/research-line/prescriptive-process-analytics/index.md)

## Abstract

Business Process Simulation (BPS) provides a controlled environment to evaluate alternative process designs and perform “what-if” analyses. While several approaches exist for deriving simulation models directly from event logs, existing solutions are often limited in accessibility, configurability, and interpretability. We present ProSiT: Process Simulation Tool, a user-friendly environment for data–driven simulation. ProSiT supports (i) the ingestion of event logs and process models, (ii) automated discovery of control-flow, timing, resource, and trace attribute parameters, (iii) interactive configuration of alternative service scenarios, (iv) visualization of accuracy metrics, and (v) generation and statistical analysis of simulated event logs. ProSiT integrates state-of-the-art machine learning methods for parameter discovery in an explainable whitebox form, contrasting with existing deep learning–based approaches that act as black boxes. By combining simulation accuracy with transparency, ProSiT empowers service architects and process analysts to experiment with alternative designs, evaluate service-level impacts, and explore optimization opportunities through an intuitive graphical interface.

## Practical problem

Before changing how a service is staffed or how work is routed, analysts want to try the change in a simulation rather than on live operations. Building such simulation models by hand is slow and error-prone, so recent tools learn them from event logs, but the most accurate learners are deep neural networks whose decision logic nobody can read or edit. That defeats the purpose of what-if analysis: an architect who cannot see why the model routes a case to a particular resource cannot credibly modify that behavior to test an alternative. Analysts are thus left choosing between manually built models they understand and automatically discovered ones they cannot inspect, and most research prototypes are hard to install and configure in the first place.

## Research questions

1. How can data-driven discovery of simulation parameters be made transparent enough that analysts can inspect, validate, and modify every parameter before running what-if scenarios?
2. How can log ingestion, parameter discovery, scenario configuration, accuracy assessment, and simulation be offered as one accessible end-to-end workflow?

## Contributions

- `ProSiT` (Process Simulation Tool), a web-based client-server application with a Flask backend, HTML/CSS/JavaScript frontend, SQLite persistence, and Docker distribution, with source code on GitHub.
- A five-phase workflow: ingestion of XES logs and optional PNML models, parameter discovery, interactive scenario configuration, accuracy assessment, and simulation producing a downloadable synthetic log.
- White-box parameter discovery based on probabilistic decision trees of controllable depth, covering transition weights, resource assignment, multitasking and calendars, arrival, execution and waiting time distributions, and trace attributes, with optional incremental discovery.
- An accuracy assessment module comparing simulated and real logs by n-gram control-flow distance, temporal distribution metrics, resource handover realism, and entropy-based generalization.
- A RESTful API (upload, discover, get and update parameters, simulate) enabling integration with external systems alongside the graphical interface.

## Key findings

- This is a tool demonstration paper; no quantitative evaluation is reported, and the paper refers to companion work for the accuracy of the underlying white-box discovery method.
- A demonstration video walks through the complete workflow on the BPI Challenge 2017 event log, from upload through parameter discovery, scenario editing, simulation, and result analysis.
- The tool outputs process maps annotated with performance data, cycle time distributions, resource utilization heatmaps, and activity execution and waiting time analyses; user studies on usability and trust are left to future work.