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

Detalhes bibliográficos
Autor(a) principal: Gubareva, Regina
Data de Publicação: 2024
Outros Autores: Lopes, Rui Pedro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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.
id RCAP_07a2b700dcb56a2de9ad4d857204a6a5
oai_identifier_str oai:bibliotecadigital.ipb.pt:10198/29305
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling 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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-25T12:20:55Zoai:bibliotecadigital.ipb.pt:10198/29305Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T12:34:26.554609Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
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 reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833592937986916352

Registros relacionados