基于单调性引导的自底向上发现框架，可高效构建复杂流程模型。
Monotonicity-Guided Bottom-Up Petri Net Discovery: The SPECpp Framework
- 利用单调性特性自底向上构建佩特里网，无需预设流程结构
- 在真实与合成数据上实现高质量模型，支持长时依赖和自由选择结构
- 适合需要灵活建模复杂并发行为的研究者或工业用户
流程发现是流程挖掘的核心挑战之一。佩特里网因其局部构造即可表达复杂行为（包括并发）而备受青睐。尽管全局行为分析困难，但通过单调性属性可高效刻画单个库所，支持自底向上的发现方法。与依赖预定义序列、选择、循环和并发结构的自顶向下方法（如归纳矿工）不同，本方法使这些结构自然涌现，能充分使用佩特里网的表达能力，包括自由选择结构和长时依赖。主要挑战在于候选库所及其组合的指数级数量。本文提出SPECpp框架，采用策略在时间和资源约束下获得高质量模型。SPECpp支持快速实验，并在合成与真实事件数据上评估了这些策略的有效性。
原文摘要 · Abstract (English)
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.
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