Titolo | METROFOOD-IT: A data platform proposal using Agrifood Smart Data Model |
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Tipo di pubblicazione | Presentazione a Congresso |
Anno di Pubblicazione | 2024 |
Autori | Di Bitonto, P., De Trizio L., Magarelli Rosaria Alessandra, Diacono D., Novielli P., Romano D., Zoani Claudia, Bellotti R., and Tangaro S. |
Conference Name | 2024 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd4.0 and IoT 2024 - Proceedings |
Editore | Institute of Electrical and Electronics Engineers Inc. |
Parole chiave | Agrifood, Architectural modeling, Data flow analysis, Data platform, Data Sharing, Digitisation, Distributed database, Ecosystems, Food sector, Information services, Integrated service, Semantics, Service oriented architecture (SOA), Smart data model, SMART datum |
Abstract | METROFOOD-IT enhances the Italian Node of the ESFRI METROFOOD-RI infrastructure and promotes research and innovation in the agri-food sector through integrated services, with an emphasis on digitization, efficiency, traceability, and sustainability. The project fosters a research paradigm focused on improving metrological data flows to enhance, quality, traceability, security in the food and nutritional domain. This paper introduces the architectural model utilized for the integration of both physical and electronic facilities. A service-oriented architecture (SOA) has been designed to facilitate efficient and scalable data sharing. The METROFOOD-IT architectural model unfolds across three levels: services, data infrastructure, and linking infrastructure. The data architecture plays a central role in managing backend systems for services, ensuring continuous data availability to each ecosystem service. To guarantee a stable and recognized data model by the scientific and industrial community, the Smart Data Model Agrifood has been adopted. This model addresses standardized data sharing and standardization issues, ensuring uniformity in data syntax and semantics, implementing access restrictions, and providing essential technical aspects in data platforms. © 2024 IEEE. |
URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199553123&doi=10.1109%2fMetroInd4.0IoT61288.2024.10584240&partnerID=40&md5=e2363bf3d82bb6b52c9259ba98a7aca9 |
DOI | 10.1109/MetroInd4.0IoT61288.2024.10584240 |
Citation Key | DiBitonto2024117 |