Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/92008
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dc.contributorCollege of Professional and Continuing Education-
dc.contributorCollege of Professional and Continuing Education-
dc.contributorCollege of Professional and Continuing Education-
dc.creatorWong, S-
dc.creatorYeung, JKW-
dc.creatorLau, YY-
dc.creatorSo, J-
dc.date.accessioned2022-02-07T07:04:57Z-
dc.date.available2022-02-07T07:04:57Z-
dc.identifier.urihttp://hdl.handle.net/10397/92008-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Wong, S.; Yeung, J.-K.-W.;Lau, Y.-Y.; So, J. TechnicalSustainability of Cloud-BasedBlockchain Integrated with MachineLearning for Supply ChainManagement. Sustainability 2021, 13,8270 is available at https://doi.org/10.3390/su13158270en_US
dc.subjectBlockchainen_US
dc.subjectCloud infrastructureen_US
dc.subjectData analyticsen_US
dc.subjectMachine learningen_US
dc.subjectSupply chainen_US
dc.subjectTechnical sustainabilityen_US
dc.titleTechnical sustainability of cloud-based blockchain integrated with machine learning for supply chain managementen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume13-
dc.identifier.issue15-
dc.identifier.doi10.3390/su13158270-
dcterms.abstractKnowing the challenges of keeping and manipulating more and more immutable transaction records in a blockchain network of various supply chain parties and the opportunities of leveraging sophisticated analyses on the big data generated from these records, design of a robust blockchain architecture based on a cloud infrastructure is proposed. This paper presents this technical design with consideration of the technical sustainability in terms of scalability and big data processing and analytics. A case study was used to illustrate how the technical sustainability is achieved by applying the proposed technical design to the real-time detection of the maritime risk management. This case also illustrates how machine learning mechanism helps to reduce maritime risk by guiding a cargo ship to adjust to the planned or safe route from a detour to a danger zone. This paper also discusses the implications for further research direction.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSustainability, Aug. 2021, v. 13, no. 15, 8270-
dcterms.isPartOfSustainability-
dcterms.issued2021-08-
dc.identifier.scopus2-s2.0-85111421431-
dc.identifier.eissn2071-1050-
dc.identifier.artn8270-
dc.description.validate202202 bcvc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextFunding: The publication of this research was substantially funded by the College of Professional and Continuing Education.en_US
dc.description.pubStatusPublisheden_US
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