Kuefner, Thomas and Schönig, Stefan and Jasinski, Richard and Ermer, Andreas (2021) Vertical data continuity with lean edge analytics for industry 4.0 production. COMPUTERS IN INDUSTRY, 125: 103389. ISSN 0166-3615, 1872-6194
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Industry 4.0 is characterized by the digitization and networking of machines and systems in production. The amount of data in production is increasing, providing information about processes and thus enables the autonomous monitoring, control and optimization of value creation processes. However, there have been several open challenges and current research questions identified. In particular, new solutions need to be scalable and high-performing to deal with the growing volumes of data close to real-time. The work at hand tackles these research gaps by presenting an approach to realize vertical data continuity by combining signal acquisition and simultaneous data evaluation in a decentralized system without the use of time-consuming external cloud solutions. The approach has been evaluated in laboratory as well as in industrial settings. (C) 2020 Elsevier B.V. All rights reserved.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Edge analytics; Industry 4.0; Smart sensors; Machine learning |
| Subjects: | 000 Computer science, information & general works > 004 Computer science 300 Social sciences > 330 Economics |
| Divisions: | Business, Economics and Information Systems > Institut für Wirtschaftsinformatik > Professur für Wirtschaftsinformatik insbesondere IoT-basierte Informationssysteme – Prof. Dr. Stefan Schönig |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 07 Jul 2022 09:57 |
| Last Modified: | 07 Jul 2022 09:57 |
| URI: | https://pred.uni-regensburg.de/id/eprint/45976 |
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