Linking Actor Behavior to Process Performance Over Time
This work addresses the need for more nuanced, actor-centric methods in process mining to better capture temporal dependencies in real-world processes, though it appears incremental as it combines existing techniques like Granger causality and Group Lasso in a new application.
The paper tackled the problem of understanding how individual actor behavior influences process performance over time, which traditional aggregate and static approaches overlook. By integrating actor behavior analysis with Granger causality on real-world event logs, they found that actor behavior has direct and measurable impacts on process performance, particularly throughput time, with a small set of lags capturing the majority of causal influence.
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.