Εμφάνιση απλής εγγραφής

dc.creatorLeal F., Chis A.E., Caton S., González–Vélez H., García–Gómez J.M., Durá M., Sánchez–García A., Sáez C., Karageorgos A., Gerogiannis V.C., Xenakis A., Lallas E., Ntounas T., Vasileiou E., Mountzouris G., Otti B., Pucci P., Papini R., Cerrai D., Mier M.en
dc.date.accessioned2023-01-31T08:49:22Z
dc.date.available2023-01-31T08:49:22Z
dc.date.issued2021
dc.identifier10.1016/j.bdr.2020.100172
dc.identifier.issn22145796
dc.identifier.urihttp://hdl.handle.net/11615/75732
dc.description.abstractProduction lines in pharmaceutical manufacturing generate numerous heterogeneous data sets from various embedded systems which control the multiple processes of medicine production. Such data sets should arguably ensure end-to-end traceability and data integrity in order to release a medicine batch, which is uniquely identified and tracked by its batch number/code. Consequently, auditable computerised systems are crucial on pharmaceutical production lines, since the industry is becoming increasingly regulated for product quality and patient health purposes. This paper describes the EU-funded SPuMoNI project, which aims to ensure the quality of large amounts of data produced by computerised production systems in representative pharmaceutical environments. Our initial results include significant progress in: (i) end-to-end verification taking advantage of blockchain properties and smart contracts to ensure data authenticity, transparency, and immutability; (ii) data quality assessment models to identify data behavioural patterns that can violate industry practices and/or international regulations; and (iii) intelligent agents to collect and manipulate data as well as perform smart decisions. By analysing multiple sensors in medicine production lines, manufacturing work centres, and quality control laboratories, our approach has been initially evaluated using representative industry-grade pharmaceutical manufacturing data sets generated at an IT environment with regulated processes inspected by regulatory and government agencies. © 2021 The Author(s)en
dc.language.isoenen
dc.sourceBig Data Researchen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85099646510&doi=10.1016%2fj.bdr.2020.100172&partnerID=40&md5=023dc76d4de89f666e767d060ae7c124
dc.subjectBlockchainen
dc.subjectEmbedded systemsen
dc.subjectIntelligent agentsen
dc.subjectProcess controlen
dc.subjectALCOAen
dc.subjectBlock-chainen
dc.subjectData anayticen
dc.subjectData integrityen
dc.subjectData qualityen
dc.subjectData seten
dc.subjectEnd to enden
dc.subjectHeterogeneous dataen
dc.subjectPharmaceutical manufacturingen
dc.subjectProduction lineen
dc.subjectSmart contracten
dc.subjectElsevier Inc.en
dc.titleSmart Pharmaceutical Manufacturing: Ensuring End-to-End Traceability and Data Integrity in Medicine Productionen
dc.typejournalArticleen


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Εμφάνιση απλής εγγραφής