Discriminative Rule Learning for Outcome-Guided Process Model Discovery

2025-10-31

AI Summary

Traditional process mining often overlooks outcome heterogeneity, hindering the identification of critical behavioral patterns. To address this, we propose an outcome-guided process discovery framework. First, it automatically partitions event log traces into ideal and non-ideal executions based on observed outcomes. Second, it learns interpretable discriminative rules to group traces by control-flow characteristics, enabling the generation of focused, human-readable, and behaviorally differentiated process models. Finally, it separately models and visually analyzes the drivers of desirable versus undesirable executions. The framework integrates control-flow feature extraction, rule-based learning, and grouped process discovery, and is empirically validated on multiple real-world business logs. It significantly improves accuracy in anomaly pattern detection, compliance analysis, and performance diagnostics. An open-source implementation is publicly available.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Distinguishing desirable and undesirable process executions from event logs
• Discovering outcome-aware process models for conformance checking
• Learning discriminative rules to reveal drivers of process outcomes

Innovation

• Learning discriminative rules for trace grouping
• Applying process discovery within each group
• Creating focused interpretable models for outcomes