实时发现动态业务流程的仿真模型，兼顾新旧数据。
Online Discovery of Simulation Models for Evolving Business Processes (Extended Version)
- 结合增量式流程发现与在线学习，动态更新模型。
- 实验显示重视近期数据可提升仿真稳定性与抗概念漂移能力。
- 适合需要持续优化流程的实时业务系统开发者。
业务流程仿真（BPS）旨在模拟业务流程的动态行为。已有大量方法可从历史事件日志中自动发现仿真模型，降低人工设计成本。然而，在动态业务环境中，组织不断优化流程以提升效率、降低成本并改善客户满意度。现有仿真发现技术难以适应实时运营变化。本文提出一种流式流程仿真发现技术，融合增量式流程发现与在线机器学习方法，优先考虑近期数据同时保留历史信息，确保对演化过程动态的适应性。在四个不同事件日志上的实验表明，赋予近期数据更高权重并保留历史知识对仿真至关重要。所提方法不仅生成更稳定的仿真结果，且在概念漂移场景下表现出强鲁棒性，如一个用例所示。
原文摘要 · Abstract (English)
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.
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