发表机构
University of Mannheim; SAP Signavio（曼海姆大学; 思爱普 Signavio）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对业务流程延迟检测的内在难度，分析14个事件日志后发现高延迟案例识别困难源于分布偏斜与不确定性，提出利用相关性的改进方法，为PPM研究提供新方向。
AI 中文摘要
业务流程中延迟案例的早期检测是组织的关键能力。预测过程监控（PPM）通过使用历史事件日志预测正在进行案例的剩余时间来支持此任务，从而实现及时干预以避免错过截止日期和违反服务水平。尽管剩余时间预测通过复杂的深度学习架构取得了显著进展，但人们对延迟检测本身的内在难度知之甚少。由于性能通常使用聚合指标进行评估，现有工作无法深入了解模型在目标分布上的表现，尤其是在具有大延迟的操作关键案例上。在本文中，我们通过分析延迟检测的难度来解决这一差距。在14个事件日志中，我们发现剩余时间通常呈现强烈的右偏分布，只有小部分案例表现出大延迟。现有模型能很好地捕捉该分布的众数，但在高延迟案例上表现不佳。我们进一步发现明显的异方差性，表明预测不确定性随延迟幅度增加而增大。基于这些发现，我们评估了缓解不平衡问题的方法，但仅发现有限的益处，这表明关键潜在问题可能不是不平衡，而是与延迟案例相关的更高不确定性。我们证明这种相关性可被利用来大幅改进延迟案例的识别。总体而言，我们的工作为延迟检测难度的来源提供了新见解，并将不确定性感知建模确定为未来PPM研究的有前景方向。
英文摘要
The early detection of delayed cases in business processes is a critical capability for organizations. Predictive process monitoring (PPM) supports this task by using historical event logs to predict the remaining time of ongoing cases, enabling timely interventions to avoid missed deadlines and service level violations. Although remaining time prediction has advanced considerably through sophisticated deep learning architectures, little is known about the intrinsic difficulty of delay detection itself. Since performance is typically assessed using aggregate metrics, prior work provides limited insight into how models perform across the target distribution, especially on the operationally most critical cases with large delays. In this paper, we address this gap by analyzing the difficulty of delay detection. Across 14 event logs, we show that remaining times are typically strongly right-skewed, with only a small fraction of cases exhibiting large delays. Existing models capture the mode of this distribution well but perform poorly on high-delay cases. We further uncover pronounced heteroscedasticity, showing that predictive uncertainty increases with delay magnitude. Based on these findings, we evaluate approaches to mitigate the imbalance problem, but find only limited benefits, suggesting that the key underlying problem may not be imbalance but higher uncertainty associated with delayed cases. We show that this correlation can be exploited to substantially improve the identification of delayed cases. Overall, our work provides new insights into the sources of difficulty in delay detection and identifies uncertainty-aware modeling as a promising direction for future PPM research.
CommentsBusiness Process Management Conference
