Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing
The use of mature, reliable, and validated solutions can save significant time and cost when introducing new technologies to companies. Reference Architectures represent such best-practice techniques and have the potential to increase the speed and reliability of the development process in many appl...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/14/4/114 |
| _version_ | 1797504772632215552 |
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| author | Attila Csaba Marosi Márk Emodi Ákos Hajnal Róbert Lovas Tamás Kiss Valerie Poser Jibinraj Antony Simon Bergweiler Hamed Hamzeh James Deslauriers József Kovács |
| author_facet | Attila Csaba Marosi Márk Emodi Ákos Hajnal Róbert Lovas Tamás Kiss Valerie Poser Jibinraj Antony Simon Bergweiler Hamed Hamzeh James Deslauriers József Kovács |
| author_sort | Attila Csaba Marosi |
| collection | DOAJ |
| description | The use of mature, reliable, and validated solutions can save significant time and cost when introducing new technologies to companies. Reference Architectures represent such best-practice techniques and have the potential to increase the speed and reliability of the development process in many application domains. One area where Reference Architectures are increasingly utilized is cloud-based systems. Exploiting the high-performance computing capability offered by clouds, while keeping sovereignty and governance of proprietary information assets can be challenging. This paper explores how Reference Architectures can be applied to overcome this challenge when developing cloud-based applications. The presented approach was developed within the DIGITbrain European project, which aims at supporting small and medium-sized enterprises (SMEs) and mid-caps in realizing smart business models called Manufacturing as a Service, via the efficient utilization of Digital Twins. In this paper, an overview of Reference Architecture concepts, as well as their classification, specialization, and particular application possibilities are presented. Various data management and potentially spatially detached data processing configurations are discussed, with special attention to machine learning techniques, which are of high interest within various sectors, including manufacturing. A framework that enables the deployment and orchestration of such overall data analytics Reference Architectures in clouds resources is also presented, followed by a demonstrative application example where the applicability of the introduced techniques and solutions are showcased in practice. |
| first_indexed | 2024-03-10T04:09:07Z |
| format | Article |
| id | doaj.art-5d6de5ebdebc4be3a4fa2aa68f841d72 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T04:09:07Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-5d6de5ebdebc4be3a4fa2aa68f841d722023-11-23T08:15:58ZengMDPI AGFuture Internet1999-59032022-04-0114411410.3390/fi14040114Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided ManufacturingAttila Csaba Marosi0Márk Emodi1Ákos Hajnal2Róbert Lovas3Tamás Kiss4Valerie Poser5Jibinraj Antony6Simon Bergweiler7Hamed Hamzeh8James Deslauriers9József Kovács10Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Kende u. 13-17, 1111 Budapest, HungaryInstitute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Kende u. 13-17, 1111 Budapest, HungaryInstitute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Kende u. 13-17, 1111 Budapest, HungaryInstitute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Kende u. 13-17, 1111 Budapest, HungaryCentre for Parallel Computing, University of Westminster, 115 New Cavendish Street, London W1W 6UW, UKGerman Research Center for Artificial Intelligence (DFKI), Trippstadter Str. 122, 67663 Kaiserslautern, GermanyGerman Research Center for Artificial Intelligence (DFKI), Trippstadter Str. 122, 67663 Kaiserslautern, GermanyGerman Research Center for Artificial Intelligence (DFKI), Trippstadter Str. 122, 67663 Kaiserslautern, GermanyCentre for Parallel Computing, University of Westminster, 115 New Cavendish Street, London W1W 6UW, UKCentre for Parallel Computing, University of Westminster, 115 New Cavendish Street, London W1W 6UW, UKInstitute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Kende u. 13-17, 1111 Budapest, HungaryThe use of mature, reliable, and validated solutions can save significant time and cost when introducing new technologies to companies. Reference Architectures represent such best-practice techniques and have the potential to increase the speed and reliability of the development process in many application domains. One area where Reference Architectures are increasingly utilized is cloud-based systems. Exploiting the high-performance computing capability offered by clouds, while keeping sovereignty and governance of proprietary information assets can be challenging. This paper explores how Reference Architectures can be applied to overcome this challenge when developing cloud-based applications. The presented approach was developed within the DIGITbrain European project, which aims at supporting small and medium-sized enterprises (SMEs) and mid-caps in realizing smart business models called Manufacturing as a Service, via the efficient utilization of Digital Twins. In this paper, an overview of Reference Architecture concepts, as well as their classification, specialization, and particular application possibilities are presented. Various data management and potentially spatially detached data processing configurations are discussed, with special attention to machine learning techniques, which are of high interest within various sectors, including manufacturing. A framework that enables the deployment and orchestration of such overall data analytics Reference Architectures in clouds resources is also presented, followed by a demonstrative application example where the applicability of the introduced techniques and solutions are showcased in practice.https://www.mdpi.com/1999-5903/14/4/114IoTdigital twinreference architecturemicroservicealgorithmanalytics |
| spellingShingle | Attila Csaba Marosi Márk Emodi Ákos Hajnal Róbert Lovas Tamás Kiss Valerie Poser Jibinraj Antony Simon Bergweiler Hamed Hamzeh James Deslauriers József Kovács Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing Future Internet IoT digital twin reference architecture microservice algorithm analytics |
| title | Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing |
| title_full | Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing |
| title_fullStr | Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing |
| title_full_unstemmed | Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing |
| title_short | Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing |
| title_sort | interoperable data analytics reference architectures empowering digital twin aided manufacturing |
| topic | IoT digital twin reference architecture microservice algorithm analytics |
| url | https://www.mdpi.com/1999-5903/14/4/114 |
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