Linking Actor Behavior to Process Performance Over Time

2025-07-30

AI Summary

Traditional process analysis relies on static, aggregated data, failing to capture the causal dynamics of individual behavior evolution over time. This paper proposes a novel method integrating temporal behavioral modeling with Granger causality inference. Specifically, it is the first to combine fine-grained temporal patterns of actor interactions with Group Lasso-based lag selection to automatically identify key lagged behavioral structures exerting persistent causal effects on process performance metrics—such as throughput time—directly from event logs. The approach breaks away from prevailing static assumptions about behavior–outcome relationships. Empirical evaluation on real-world industrial data reveals statistically significant and quantifiable cross-temporal causal impacts of individual behaviors—including handover delays and task interruptions—on downstream process outcomes. By uncovering interpretable, intervention-ready behavioral-level causal mechanisms, the method provides a rigorous foundation for data-driven process optimization.

Technology Category

• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Reasoning under Uncertainty: Causality

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

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.

Problem

• Analyzing how actor behavior affects process performance dynamically
• Overcoming limitations of static process data in capturing causality
• Identifying temporal dependencies between actor interactions and throughput time

Innovation

• Integrates actor behavior with Granger causality
• Uses Group Lasso for lag selection
• Applies time series to actor interactions