Anomalies in complex industrial processes are often obscured by high variability and complexity of event data, which hinders their identification and interpretation using process mining. To address this problem, we introduce WISE (Weighted Insights for Evaluating Efficiency), a novel method for analyzing business process metrics through the integration of domain knowledge, process mining, and machine learning. The methodology involves defining business goals and establishing Process Norms with weighted constraints at the activity level, incorporating input from domain experts and process analysts. Individual process instances are scored based on these constraints, and the scores are normalized to identify features impacting process goals. Evaluation using the BPIC 2019 dataset and real industrial contexts demonstrates that WISE enhances automation in business process analysis and effectively detects deviations from desired process flows. While LLMs support the analysis, the inclusion of domain experts ensures the accuracy and relevance of the findings.

翻译：复杂工业流程中的异常通常被事件数据的高变异性与复杂性所掩盖，这阻碍了利用流程挖掘技术对其进行识别与解释。为解决这一问题，我们提出了WISE（加权效率评估洞察），这是一种通过整合领域知识、流程挖掘与机器学习来分析业务流程度量的新方法。该方法包含定义业务目标，并在活动层级建立具有加权约束的流程规范，同时纳入领域专家与流程分析师的专业意见。基于这些约束对单个流程实例进行评分，并通过分数归一化来识别影响流程目标的关键特征。使用BPIC 2019数据集及真实工业场景的评估表明，WISE提升了业务流程分析的自动化水平，并能有效检测与期望流程的偏差。尽管大语言模型为分析提供支持，但领域专家的参与确保了研究结果的准确性与相关性。