Literature review on the smart city resources analysis with big data methodologies
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/29305 |
Resumo: | This 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. |
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Literature review on the smart city resources analysis with big data methodologiesBig dataSmart cityResources consumptionThis 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.Open access funding provided by FCT|FCCN (b-on).Springer NatureBiblioteca Digital do IPBGubareva, ReginaLopes, Rui Pedro2024-01-24T09:24:02Z20242024-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/29305engGubareva, 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. 1-1310.1007/s42979-023-02457-x2661-8907info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-31T01:19:40Zoai:bibliotecadigital.ipb.pt:10198/29305Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:58:58.372747Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Literature review on the smart city resources analysis with big data methodologies |
title |
Literature review on the smart city resources analysis with big data methodologies |
spellingShingle |
Literature review on the smart city resources analysis with big data methodologies Gubareva, Regina Big data Smart city Resources consumption |
title_short |
Literature review on the smart city resources analysis with big data methodologies |
title_full |
Literature review on the smart city resources analysis with big data methodologies |
title_fullStr |
Literature review on the smart city resources analysis with big data methodologies |
title_full_unstemmed |
Literature review on the smart city resources analysis with big data methodologies |
title_sort |
Literature review on the smart city resources analysis with big data methodologies |
author |
Gubareva, Regina |
author_facet |
Gubareva, Regina Lopes, Rui Pedro |
author_role |
author |
author2 |
Lopes, Rui Pedro |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Gubareva, Regina Lopes, Rui Pedro |
dc.subject.por.fl_str_mv |
Big data Smart city Resources consumption |
topic |
Big data Smart city Resources consumption |
description |
This 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. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-01-24T09:24:02Z 2024 2024-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10198/29305 |
url |
http://hdl.handle.net/10198/29305 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Gubareva, 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. 1-13 10.1007/s42979-023-02457-x 2661-8907 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer Nature |
publisher.none.fl_str_mv |
Springer Nature |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799137074182356992 |