标题： 空间过程挖掘 显示英文标题

标题： Spatial Process Mining

摘要： 我们提出了一种新的框架，该框架专注于数字孪生中的现场实体，数字孪生是现实世界与数字空间的配对。 其特性包括主动感知以生成事件日志、复杂过程的空间和时间划分，以及可扩展到空间和时间的过程可视化和分析。 作为一个具体例子，一个细胞生产系统由制造过程中称为单元的连接制造空间组成。 单元通过天花板摄像机进行感应，生成甘特图，根据单元内发生的事件周期提供过程的鸟瞰视图。 对于有经验的操作员来说，这个甘特图易于理解，但我们还提出了一种方法，用于找出偏离正常过程的原因焦点，而无需特殊的经验或知识。 这种方法通过使用我们自己的事件节点排名算法捕获单元内发生的过程特征，该算法是对HITS（超文本诱导主题选择）的修改，它根据从过程模型生成的复杂网络对网页进行评分。 显示英文摘要

摘要： We propose a new framework that focuses on on-site entities in the digital twin, a pairing of the real world and digital space. Characteristics include active sensing to generate event logs, spatial and temporal partitioning of complex processes, and visualization and analysis of processes that can be scaled in space and time. As a specific example, a cell production system is composed of connected manufacturing spaces called cells in a manufacturing process. A cell is sensed by ceiling cameras to generate a Gantt chart that provides a bird's-eye view of the process according to the cycle of events that occur in the cell. This Gantt chart is easy to understand for experienced operators, but we also propose a method for finding the focus of causes of deviations from the usual process without special experience or knowledge. This method captures the characteristics of the processes occurring in a cell by using our own event node ranking algorithm, a modification of HITS (Hypertext Induced Topic Selection), which scores web pages against a complex network generated from a process model.