实现流程模型与事件数据的同步抽象，保持真实行为关联性。
Synchronizing Process Model and Event Abstraction for Grounded Process Intelligence (Extended Version)
- 通过行为特征抽象实现模型与事件数据的协同简化
- 证明抽象后重建的模型与原模型等价，保证语义一致性
- 适合需多轮抽象仍保留真实行为洞察的流程分析场景
模型抽象（MA）和事件抽象（EA）是降低已发现模型与事件数据复杂度的有效手段。在流程智能项目中，常需对从事件日志中挖掘出的模型进行多次抽象以达成优化目标（如减小模型规模）。然而，现有方法在模型抽象后缺乏对底层事件日志的同步抽象技术，导致失去真实世界行为的根基，限制分析深度。本文为此提供形式化基础：证明通过模型抽象（MA）与从抽象事件日志中重新发现模型，可得到等价过程模型。基于非有序保留的MA技术（行为特征抽象），本文提出一种新型事件抽象方法，验证了该同步抽象路径的可行性。
原文摘要 · Abstract (English)
Model abstraction (MA) and event abstraction (EA) are means to reduce complexity of (discovered) models and event data. Imagine a process intelligence project that aims to analyze a model discovered from event data which is further abstracted, possibly multiple times, to reach optimality goals, e.g., reducing model size. So far, after discovering the model, there is no technique that enables the synchronized abstraction of the underlying event log. This results in loosing the grounding in the real-world behavior contained in the log and, in turn, restricts analysis insights. Hence, in this work, we provide the formal basis for synchronized model and event abstraction, i.e., we prove that abstracting a process model by MA and discovering a process model from an abstracted event log yields an equivalent process model. We prove the feasibility of our approach based on behavioral profile abstraction as non-order preserving MA technique, resulting in a novel EA technique.
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