Join Operation for Semantic Data Enrichment of Asynchronous Time Series Data

[EN] In this paper, we present a novel framework for enriching time series data in smart cities by supplementing it with information from external sources via semantic data enrichment. Our methodology effectively merges multiple data sources into a uniform time series, while addressing difficulties...

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Bibliographic Details
Authors: García-Climent, Eloi, Peyman, Mohammad, Serrat, Carles, Xhafa, Fatos
Format: article
Publication Date:2023
Country:España
Institution:Universitat Politècnica de València (UPV)
Repository:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Language:English
OAI Identifier:oai:riunet.upv.es:10251/205510
Online Access:https://riunet.upv.es/handle/10251/205510
Access Level:Open access
Keyword:Join operation
Data standardization
Spatial data distribution
Lagged cross-correlations
Time series data
Semantic data enrichment
Open Data Barcelona
Smart City
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spelling Join Operation for Semantic Data Enrichment of Asynchronous Time Series DataGarcía-Climent, EloiPeyman, MohammadSerrat, CarlesXhafa, FatosJoin operationData standardizationSpatial data distributionLagged cross-correlationsTime series dataSemantic data enrichmentOpen Data BarcelonaSmart City[EN] In this paper, we present a novel framework for enriching time series data in smart cities by supplementing it with information from external sources via semantic data enrichment. Our methodology effectively merges multiple data sources into a uniform time series, while addressing difficulties such as data quality, contextual information, and time lapses. We demonstrate the efficacy of our method through a case study in Barcelona, which permitted the use of advanced analysis methods such as windowed cross-correlation and peak picking. The resulting time series data can be used to determine traffic patterns and has potential uses in other smart city sectors, such as air quality, energy efficiency, and public safety. Interactive dashboards enable stakeholders to visualize and summarize key insights and patterns.This work was partially funded by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033), as well as by the Barcelona City Council and Fundació la Caixa under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001).MDPI AGMinisterio de Ciencia e InnovaciónFundació Bancària Caixa d'Estalvis i Pensions de BarcelonaRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-04-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/205510reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-111100RB-C21 ALGORITMOS AGILES, INTERNET DE LAS COSAS, Y ANALITICA DE DATOS PARA UN TRANSPORTE SOSTENIBLE EN CIUDADES INTELIGENTESFundació Bancària Caixa d'Estalvis i Pensions de Barcelona Fundació Bancària Caixa d'Estalvis i Pensions de Barcelona 21S09355-001open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2055102026-06-13T07:49:27Z
dc.title.none.fl_str_mv Join Operation for Semantic Data Enrichment of Asynchronous Time Series Data
title Join Operation for Semantic Data Enrichment of Asynchronous Time Series Data
spellingShingle Join Operation for Semantic Data Enrichment of Asynchronous Time Series Data
García-Climent, Eloi
Join operation
Data standardization
Spatial data distribution
Lagged cross-correlations
Time series data
Semantic data enrichment
Open Data Barcelona
Smart City
title_short Join Operation for Semantic Data Enrichment of Asynchronous Time Series Data
title_full Join Operation for Semantic Data Enrichment of Asynchronous Time Series Data
title_fullStr Join Operation for Semantic Data Enrichment of Asynchronous Time Series Data
title_full_unstemmed Join Operation for Semantic Data Enrichment of Asynchronous Time Series Data
title_sort Join Operation for Semantic Data Enrichment of Asynchronous Time Series Data
dc.creator.none.fl_str_mv García-Climent, Eloi
Peyman, Mohammad
Serrat, Carles
Xhafa, Fatos
author García-Climent, Eloi
author_facet García-Climent, Eloi
Peyman, Mohammad
Serrat, Carles
Xhafa, Fatos
author_role author
author2 Peyman, Mohammad
Serrat, Carles
Xhafa, Fatos
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación
Fundació Bancària Caixa d'Estalvis i Pensions de Barcelona
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Join operation
Data standardization
Spatial data distribution
Lagged cross-correlations
Time series data
Semantic data enrichment
Open Data Barcelona
Smart City
topic Join operation
Data standardization
Spatial data distribution
Lagged cross-correlations
Time series data
Semantic data enrichment
Open Data Barcelona
Smart City
description [EN] In this paper, we present a novel framework for enriching time series data in smart cities by supplementing it with information from external sources via semantic data enrichment. Our methodology effectively merges multiple data sources into a uniform time series, while addressing difficulties such as data quality, contextual information, and time lapses. We demonstrate the efficacy of our method through a case study in Barcelona, which permitted the use of advanced analysis methods such as windowed cross-correlation and peak picking. The resulting time series data can be used to determine traffic patterns and has potential uses in other smart city sectors, such as air quality, energy efficiency, and public safety. Interactive dashboards enable stakeholders to visualize and summarize key insights and patterns.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-04-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/205510
url https://riunet.upv.es/handle/10251/205510
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-111100RB-C21 ALGORITMOS AGILES, INTERNET DE LAS COSAS, Y ANALITICA DE DATOS PARA UN TRANSPORTE SOSTENIBLE EN CIUDADES INTELIGENTES
Fundació Bancària Caixa d'Estalvis i Pensions de Barcelona Fundació Bancària Caixa d'Estalvis i Pensions de Barcelona 21S09355-001
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869411838878285824
score 15,812455

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