Designing a System Architecture for Dynamic Data Collection as a Foundation for Knowledge Modeling in Industry
This study develops and implements a scalable system architecture for dynamic data acquisition and knowledge modeling in industrial contexts. The objective is to efficiently process large datasets to support decision-making and process optimization within Industry 4.0. The architecture integrates mo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/5081 |
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| _version_ | 1850155880277868544 |
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| author | Edmund Radlbauer Thomas Moser Markus Wagner |
| author_facet | Edmund Radlbauer Thomas Moser Markus Wagner |
| author_sort | Edmund Radlbauer |
| collection | DOAJ |
| description | This study develops and implements a scalable system architecture for dynamic data acquisition and knowledge modeling in industrial contexts. The objective is to efficiently process large datasets to support decision-making and process optimization within Industry 4.0. The architecture integrates modern technologies, such as the ibaPDA system for data acquisition, and employs communication standards like Modbus TCP and OPC UA to ensure broad compatibility with diverse equipment. In addition, it leverages native protocols offered by certain controllers, enabling direct data exchange without the need for conversion layers. A developed prototype demonstrates the practical applicability of the architecture, tested in a real industrial environment with a focus on processing speed, data integrity, and system reliability. The results indicate that the architecture not only meets the requirements for dynamic data acquisition but also enhances knowledge modeling. This leads to more efficient process control and opens new perspectives for managing and analyzing big data in production environments. The study emphasizes the importance of an integrated development approach and highlights the need for interdisciplinary collaboration to address operational challenges. Future extensions may include the implementation of Python interfaces and machine learning algorithms for data simulation, enabling more accurate predictive models. These findings provide valuable insights for industry, software development, data science, and academia, helping to tackle the challenges of Industry 4.0 and drive innovation forward. |
| format | Article |
| id | doaj-art-2e6bd424a8a348219f5ba5e3a6afb5de |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2e6bd424a8a348219f5ba5e3a6afb5de2025-08-20T02:24:45ZengMDPI AGApplied Sciences2076-34172025-05-01159508110.3390/app15095081Designing a System Architecture for Dynamic Data Collection as a Foundation for Knowledge Modeling in IndustryEdmund Radlbauer0Thomas Moser1Markus Wagner2Josef Ressel Centre for Knowledge-Assisted Visual Analytics for Industrial Manufacturing Data, St. Pölten University of Applied Sciences, 3100 St. Pölten, AustriaJosef Ressel Centre for Knowledge-Assisted Visual Analytics for Industrial Manufacturing Data, St. Pölten University of Applied Sciences, 3100 St. Pölten, AustriaJosef Ressel Centre for Knowledge-Assisted Visual Analytics for Industrial Manufacturing Data, St. Pölten University of Applied Sciences, 3100 St. Pölten, AustriaThis study develops and implements a scalable system architecture for dynamic data acquisition and knowledge modeling in industrial contexts. The objective is to efficiently process large datasets to support decision-making and process optimization within Industry 4.0. The architecture integrates modern technologies, such as the ibaPDA system for data acquisition, and employs communication standards like Modbus TCP and OPC UA to ensure broad compatibility with diverse equipment. In addition, it leverages native protocols offered by certain controllers, enabling direct data exchange without the need for conversion layers. A developed prototype demonstrates the practical applicability of the architecture, tested in a real industrial environment with a focus on processing speed, data integrity, and system reliability. The results indicate that the architecture not only meets the requirements for dynamic data acquisition but also enhances knowledge modeling. This leads to more efficient process control and opens new perspectives for managing and analyzing big data in production environments. The study emphasizes the importance of an integrated development approach and highlights the need for interdisciplinary collaboration to address operational challenges. Future extensions may include the implementation of Python interfaces and machine learning algorithms for data simulation, enabling more accurate predictive models. These findings provide valuable insights for industry, software development, data science, and academia, helping to tackle the challenges of Industry 4.0 and drive innovation forward.https://www.mdpi.com/2076-3417/15/9/5081Industry 4.0knowledge generationknowledge modelingprocess optimizationpredictive modelsdata integration |
| spellingShingle | Edmund Radlbauer Thomas Moser Markus Wagner Designing a System Architecture for Dynamic Data Collection as a Foundation for Knowledge Modeling in Industry Applied Sciences Industry 4.0 knowledge generation knowledge modeling process optimization predictive models data integration |
| title | Designing a System Architecture for Dynamic Data Collection as a Foundation for Knowledge Modeling in Industry |
| title_full | Designing a System Architecture for Dynamic Data Collection as a Foundation for Knowledge Modeling in Industry |
| title_fullStr | Designing a System Architecture for Dynamic Data Collection as a Foundation for Knowledge Modeling in Industry |
| title_full_unstemmed | Designing a System Architecture for Dynamic Data Collection as a Foundation for Knowledge Modeling in Industry |
| title_short | Designing a System Architecture for Dynamic Data Collection as a Foundation for Knowledge Modeling in Industry |
| title_sort | designing a system architecture for dynamic data collection as a foundation for knowledge modeling in industry |
| topic | Industry 4.0 knowledge generation knowledge modeling process optimization predictive models data integration |
| url | https://www.mdpi.com/2076-3417/15/9/5081 |
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