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.

翻译：从信息系统中提取的事件日志为理解和改进业务流程提供了丰富的基础。在许多实际应用中，可以区分理想与不理想的过程执行：理想轨迹反映高效或合规的行为，而不理想轨迹可能涉及效率低下、规则违反、延迟或资源浪费。这种区分为以更具结果意识的方式引导过程发现提供了机会。不考虑结果而发现单一过程模型可能产生不适合合规性检查和性能分析的表示，因为它们未能捕捉关键的行为差异。此外，优先考虑一种行为可能掩盖对理解过程结果至关重要的结构差异。通过学习基于控制流特征的可解释判别式规则，我们将具有相似理想性特征的轨迹分组，并在每组内分别应用过程发现。这产生了聚焦且可解释的模型，揭示了理想与不理想执行的驱动因素。该方法已实现为一个公开可用的工具，并在多个真实事件日志上进行了评估，证明了其在隔离和可视化关键过程模式方面的有效性。