Discriminative Rule Learning for Outcome-Guided Process Model Discovery
This work addresses the need for outcome-aware process model discovery in business process management, offering a more interpretable approach for analyzing and improving efficiency and compliance, though it appears incremental as it builds on existing process discovery methods.
The paper tackles the problem of discovering process models from event logs without considering outcome distinctions, which can lead to poor representations for conformance checking and performance analysis. It proposes learning discriminative rules to group traces by desirability and applying process discovery separately, resulting in focused and interpretable models that reveal drivers of both desirable and undesirable executions, with effectiveness demonstrated on multiple real-life event logs.
Event logs extracted from information systems offer a rich foundation for understanding and improving business processes. In many real-world applications, it is possible to distinguish between desirable and undesirable process executions, where desirable traces reflect efficient or compliant behavior, and undesirable ones may involve inefficiencies, rule violations, delays, or resource waste. This distinction presents an opportunity to guide process discovery in a more outcome-aware manner. Discovering a single process model without considering outcomes can yield representations poorly suited for conformance checking and performance analysis, as they fail to capture critical behavioral differences. Moreover, prioritizing one behavior over the other may obscure structural distinctions vital for understanding process outcomes. By learning interpretable discriminative rules over control-flow features, we group traces with similar desirability profiles and apply process discovery separately within each group. This results in focused and interpretable models that reveal the drivers of both desirable and undesirable executions. The approach is implemented as a publicly available tool and it is evaluated on multiple real-life event logs, demonstrating its effectiveness in isolating and visualizing critical process patterns.