Version 1
: Received: 23 May 2024 / Approved: 23 May 2024 / Online: 24 May 2024 (09:40:59 CEST)
How to cite:
Hornsteiner, M.; Empl, P.; Bunghardt, T.; Schönig, S. Reading between the Lines: Process Mining on OPC UA Network Data. Preprints2024, 2024051589. https://doi.org/10.20944/preprints202405.1589.v1
Hornsteiner, M.; Empl, P.; Bunghardt, T.; Schönig, S. Reading between the Lines: Process Mining on OPC UA Network Data. Preprints 2024, 2024051589. https://doi.org/10.20944/preprints202405.1589.v1
Hornsteiner, M.; Empl, P.; Bunghardt, T.; Schönig, S. Reading between the Lines: Process Mining on OPC UA Network Data. Preprints2024, 2024051589. https://doi.org/10.20944/preprints202405.1589.v1
APA Style
Hornsteiner, M., Empl, P., Bunghardt, T., & Schönig, S. (2024). Reading between the Lines: Process Mining on OPC UA Network Data. Preprints. https://doi.org/10.20944/preprints202405.1589.v1
Chicago/Turabian Style
Hornsteiner, M., Timo Bunghardt and Stefan Schönig. 2024 "Reading between the Lines: Process Mining on OPC UA Network Data" Preprints. https://doi.org/10.20944/preprints202405.1589.v1
Abstract
The introduction of the Industrial Internet of Things (IIoT) has led to major changes in the industry. Thanks to machine data, business process management methods and techniques could also be applied to them. However, one data source has so far remained untouched: The network data of the machines. In the business environment, process mining, for example, has already been carried out based on network data, but the IIoT, with its particular protocols such as OPC UA, has yet to be investigated. With the help of design science research and on the shoulders of CRISP-DM, we first develop a framework for process mining in the IIoT in this paper. We then apply the framework to real-world IIoT network traffic data and evaluate the outcome and performance of our approach in detail. We find tremendous potential in network traffic data but also limitations. Among other things, due to the dependence on process experts and the existence of case IDs.
Keywords
Process Mining; Industrial Internet of Things; Business Process Management; Industry 4.0
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
