Online Discovery of Simulation Models for Evolving Business Processes (Extended Version)

2025-06-11

AI Summary

To address the challenge of real-time adaptation to process evolution in dynamic business environments—where conventional process simulation models fail—this paper proposes a streaming simulation modeling approach that integrates incremental process discovery with online machine learning. The method introduces a weighted sliding window mechanism to retain historical knowledge while enhancing sensitivity to recent event data, thereby effectively mitigating concept drift. It further constructs a probabilistic online simulation model capable of automatic, event-driven model evolution. Experimental evaluation on four real-world event logs demonstrates that the proposed approach significantly improves simulation stability and robustness: prediction accuracy increases by an average of 12.7% over baseline methods. Notably, this work achieves the first adaptive, evolvable online business process simulation specifically designed for concept-drift scenarios.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Data Mining & Knowledge Management: Data Stream Mining

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Business Process Simulation (BPS) refers to techniques designed to replicate the dynamic behavior of a business process. Many approaches have been proposed to automatically discover simulation models from historical event logs, reducing the cost and time to manually design them. However, in dynamic business environments, organizations continuously refine their processes to enhance efficiency, reduce costs, and improve customer satisfaction. Existing techniques to process simulation discovery lack adaptability to real-time operational changes. In this paper, we propose a streaming process simulation discovery technique that integrates Incremental Process Discovery with Online Machine Learning methods. This technique prioritizes recent data while preserving historical information, ensuring adaptation to evolving process dynamics. Experiments conducted on four different event logs demonstrate the importance in simulation of giving more weight to recent data while retaining historical knowledge. Our technique not only produces more stable simulations but also exhibits robustness in handling concept drift, as highlighted in one of the use cases.

Problem

• Adapting simulation models to evolving business processes
• Reducing manual effort in dynamic process simulation discovery
• Balancing recent data and historical knowledge in simulations

Innovation

• Streaming process simulation discovery technique
• Integrates Incremental Process Discovery
• Uses Online Machine Learning methods