Spatial Process Mining

TL;DR

This paper introduces a spatial process mining framework utilizing active sensing and visualization in digital twins, exemplified by a cell production system, with a novel event node ranking method for deviation analysis.

Contribution

It presents a new spatial process mining framework with active sensing, visualization, and a modified HITS algorithm for analyzing complex processes in digital twins.

Findings

Effective visualization with Gantt charts for process understanding

A novel event node ranking algorithm for deviation detection

Scalable analysis of spatial and temporal process data

Abstract

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.