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Literature review on the smart city resources analysis with big data methodologies

dc.contributor.authorGubareva, Regina
dc.contributor.authorLopes, Rui Pedro
dc.date.accessioned2024-01-24T09:24:02Z
dc.date.available2024-01-24T09:24:02Z
dc.date.issued2024
dc.description.abstractThis article provides a systematic literature review on applying different algorithms to municipal data processing, aiming to understand how the data were collected, stored, pre-processed, and analyzed, to compare various methods, and to select feasible solutions for further research. Several algorithms and data types are considered, finding that clustering, classification, correlation, anomaly detection, and prediction algorithms are frequently used. As expected, the data is of several types, ranging from sensor data to images. It is a considerable challenge, although several algorithms work very well, such as Long Short-Term Memory (LSTM) for timeseries prediction and classification.pt_PT
dc.description.sponsorshipOpen access funding provided by FCT|FCCN (b-on).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGubareva, Regina; Lopes, Rui Pedro (2024). Literature review on the smart city resources analysis with big data methodologies. SN Computer Science. ISSN 2662-995X. 5:152, p. 1-13pt_PT
dc.identifier.doi10.1007/s42979-023-02457-xpt_PT
dc.identifier.eissn2661-8907
dc.identifier.urihttp://hdl.handle.net/10198/29305
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBig datapt_PT
dc.subjectSmart citypt_PT
dc.subjectResources consumptionpt_PT
dc.titleLiterature review on the smart city resources analysis with big data methodologiespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage13pt_PT
oaire.citation.issue152pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleSN Computer Sciencept_PT
oaire.citation.volume5pt_PT
person.familyNameLopes
person.givenNameRui Pedro
person.identifier.ciencia-id8E14-54E4-4DB5
person.identifier.orcid0000-0002-9170-5078
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicatione1e64423-0ec8-46ee-be96-33205c7c98a9
relation.isAuthorOfPublication.latestForDiscoverye1e64423-0ec8-46ee-be96-33205c7c98a9

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