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Reading between the Lines: Process Mining on OPC UA Network Data.
Hornsteiner, Markus; Empl, Philip; Bunghardt, Timo; Schönig, Stefan.
Afiliação
  • Hornsteiner M; Faculty of Informatics and Data Science, University of Regensburg, 93053 Regensburg, Germany.
  • Empl P; Faculty of Informatics and Data Science, University of Regensburg, 93053 Regensburg, Germany.
  • Bunghardt T; Faculty of Informatics and Data Science, University of Regensburg, 93053 Regensburg, Germany.
  • Schönig S; Faculty of Informatics and Data Science, University of Regensburg, 93053 Regensburg, Germany.
Sensors (Basel) ; 24(14)2024 Jul 11.
Article em En | MEDLINE | ID: mdl-39065898
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.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Alemanha

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Alemanha