根据日志特征和用户需求，智能推荐最适合的流程发现算法。
ProReco: A Process Discovery Recommender System
- 基于日志特性和用户偏好，自动匹配最优流程发现算法。
- 融合前沿算法并扩展特征池，提升推荐准确性。
- 结合可解释AI技术，给出推荐理由，适合流程优化人员使用。
流程发现旨在从历史执行数据（事件日志）中自动提取流程模型。尽管过去25年提出了多种流程发现算法，但尚无主导性算法。由于质量度量标准相互竞争且用户需求多样，选择最合适的算法仍具挑战性。手动为特定日志从众多算法中筛选最优方案耗时且易出错。本文提出ProReco，一个流程发现推荐系统，可根据用户偏好与日志特征推荐最适宜的算法。ProReco集成当前最先进的发现算法，扩展了先前工作的特征池，并采用可解释人工智能（XAI）技术，为推荐结果提供解释。
原文摘要 · Abstract (English)
Process discovery aims to automatically derive process models from historical execution data (event logs). While various process discovery algorithms have been proposed in the last 25 years, there is no consensus on a dominating discovery algorithm. Selecting the most suitable discovery algorithm remains a challenge due to competing quality measures and diverse user requirements. Manually selecting the most suitable process discovery algorithm from a range of options for a given event log is a time-consuming and error-prone task. This paper introduces ProReco, a Process discovery Recommender system designed to recommend the most appropriate algorithm based on user preferences and event log characteristics. ProReco incorporates state-of-the-art discovery algorithms, extends the feature pools from previous work, and utilizes eXplainable AI (XAI) techniques to provide explanations for its recommendations.
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