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Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
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Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For...
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Updated: Jun 18, 2025

Data Communication Based on MQTT in a Polymer Extrusion Process
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Reading between the Lines: Process Mining on OPC UA Network Data.

Markus Hornsteiner1, Philip Empl1, Timo Bunghardt1

  • 1Faculty of Informatics and Data Science, University of Regensburg, 93053 Regensburg, Germany.

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|July 27, 2024
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Summary

This study introduces a novel framework for process mining using Industrial Internet of Things (IIoT) network data. Findings reveal significant potential and limitations in leveraging this data for industrial process analysis.

Keywords:
business process managementindustrial IoTindustry 4.0process mining

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Area of Science:

  • Computer Science
  • Industrial Engineering
  • Data Science

Background:

  • The Industrial Internet of Things (IIoU) integrates machine data with business process management.
  • Network data from industrial machines remains an underutilized data source for process analysis.
  • Existing process mining techniques have not fully explored IIoT-specific protocols like OPC UA.

Purpose of the Study:

  • To develop and evaluate a framework for process mining in the IIoT using network traffic data.
  • To investigate the potential and limitations of analyzing IIoT network data for business process insights.

Main Methods:

  • Design Science Research methodology.
  • Adaptation of the CRISP-DM framework.
  • Application of the developed framework to real-world IIoT network traffic data.
  • Detailed evaluation of the approach's outcome and performance.

Main Results:

  • Demonstrated tremendous potential in utilizing IIoT network traffic data for process mining.
  • Identified limitations, including dependency on process experts and the need for case IDs.
  • Successfully applied a novel framework to analyze industrial network data.

Conclusions:

  • IIoT network traffic data offers significant opportunities for process mining.
  • Further research is needed to overcome identified limitations for broader adoption.
  • The developed framework provides a foundation for future IIoT process analysis.