发表机构
RWTH Aachen University（亚琛工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对过程挖掘中自顶向下Petri网发现的局限，提出SPECpp框架，通过单调性引导的自底向上策略，利用Petri网全部表达能力，在资源约束下获取高质量模型并经数据验证。
AI 中文摘要
过程发现是过程挖掘的核心挑战之一。Petri网极具吸引力，因为简单的局部结构可表达复杂行为，包括并发性。尽管其全局行为可能难以分析，但单个库所可通过单调性属性高效表征，从而实现自底向上发现。与归纳矿工等依赖序列、选择、循环和并发性预定义结构的自顶向下方法不同，我们的方法允许此类结构自然涌现，并能利用Petri网的全部表达能力，包括自由选择结构和长期依赖关系。主要挑战在于候选库所及其组合的数量呈指数级增长。我们提出SPECpp框架，该框架实现了在时间和资源约束下获取高质量模型的策略。SPECpp支持快速实验，并用于使用合成和真实事件数据评估这些策略。
英文摘要
Process discovery is one of the central challenges in process mining. Petri nets are particularly attractive because simple local constructs can express complex behavior, including concurrency. While their global behavior may be difficult to analyze, individual places can be efficiently characterized using monotonic properties, enabling bottom-up discovery. Unlike top-down approaches such as the Inductive Miner, which rely on predefined constructs for sequences, choices, loops, and concurrency, our approach allows such structures to emerge organically and can exploit the full expressive power of Petri nets, including free-choice constructs and long-term dependencies. The main challenge is the exponential number of candidate places and their combinations. We present the SPECpp framework which implements strategies to obtain high-quality models under time and resource constraints. SPECpp supports rapid experimentation and is used to evaluate these strategies using both synthetic and real-life event data.
Comments59 pages, 15 figures
