通过时间序列分析揭示个体行为如何影响流程绩效，发现行为对吞吐时间有直接且可测量的影响。
Linking Actor Behavior to Process Performance Over Time
- 结合格兰杰因果与时间序列分析，捕捉演员行为与流程结果的动态关联
- 识别出少量关键时滞，覆盖大部分因果影响，尤其显著影响吞吐时间
- 适合关注流程优化与个体行为建模的研究者或企业流程管理人员
理解个体行为如何影响流程结果是流程挖掘中的关键问题。传统方法通常依赖聚合和静态过程数据，忽略了个体行为带来的时空动态与因果关系，难以准确刻画真实流程的复杂性。本文通过将演员行为分析与格兰杰因果结合，处理真实事件日志，构建演员交互（延续、中断、交接）及流程结果的时间序列。利用组Lasso进行时滞选择，识别出一组少量但持续具影响力的时滞，揭示了演员行为对流程绩效（特别是吞吐时间）具有直接且可测量的影响。结果表明，以演员为中心的时间序列方法能有效揭示驱动流程结果的时序依赖，为理解个体行为如何影响整体效率提供了更精细的视角。
原文摘要 · Abstract (English)
Understanding how actor behavior influences process outcomes is a critical aspect of process mining. Traditional approaches often use aggregate and static process data, overlooking the temporal and causal dynamics that arise from individual actor behavior. This limits the ability to accurately capture the complexity of real-world processes, where individual actor behavior and interactions between actors significantly shape performance. In this work, we address this gap by integrating actor behavior analysis with Granger causality to identify correlating links in time series data. We apply this approach to realworld event logs, constructing time series for actor interactions, i.e. continuation, interruption, and handovers, and process outcomes. Using Group Lasso for lag selection, we identify a small but consistently influential set of lags that capture the majority of causal influence, revealing that actor behavior has direct and measurable impacts on process performance, particularly throughput time. These findings demonstrate the potential of actor-centric, time series-based methods for uncovering the temporal dependencies that drive process outcomes, offering a more nuanced understanding of how individual behaviors impact overall process efficiency.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。