基于结果导向学习判别规则，区分优劣流程行为并分别建模。
Discriminative Rule Learning for Outcome-Guided Process Model Discovery
- 通过控制流特征学习可解释的判别规则，划分不同优劣行为
- 在各行为组内独立建模，揭示优劣执行的关键模式差异
- 适合关注流程合规性、效率分析与根因诊断的从业者
从信息系统中提取的事件日志为理解与优化业务流程提供了丰富基础。在许多实际应用中，可区分理想与非理想流程执行：理想轨迹反映高效或合规行为，非理想轨迹则可能涉及低效、违规、延迟或资源浪费。这一区分使流程发现可更注重结果导向。忽略结果的单一模型难以用于合规检查与性能分析，因未能捕捉关键行为差异。同时，片面强调某类行为会掩盖结构上对结果至关重要的区别。本文通过学习控制流特征上的可解释判别规则，将轨迹按相似可接受性分组，并在每组内独立进行流程发现。这生成了聚焦且可解释的模型，揭示了理想与非理想执行的驱动因素。方法已实现为公开工具，在多个真实事件日志上评估，有效识别并可视化关键流程模式。
原文摘要 · Abstract (English)
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.
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