Discriminative Rule Learning for Outcome-Guided Process Model Discovery

TL;DR

This paper introduces a discriminative rule learning approach for process model discovery that separates desirable and undesirable process executions, leading to more interpretable and outcome-aware process models.

Contribution

It proposes a novel method that learns interpretable rules to distinguish process outcomes, enabling outcome-guided process discovery and analysis.

Findings

Effective separation of process traces based on desirability.

Enhanced interpretability of process models.

Successful application on real-life event logs.

Abstract

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.