Automated Discovery of Business Process Simulation Models from Event Logs

TL;DR

This paper introduces an automated, hyper-parameter optimized method for discovering accurate business process simulation models directly from execution logs, reducing manual tuning and improving model fidelity.

Contribution

It proposes a novel hyper-parameter optimization approach to automatically enhance the accuracy of log-based process simulation models.

Findings

The method achieves higher similarity between simulation and real logs.

It effectively automates the discovery process, reducing manual effort.

Evaluations show improved model accuracy across different domains.

Abstract

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.