Heterogeneous data source integration for smart grid ecosystems based on metadata mining

The arrival of new technologies related to smart grids and the resulting ecosystem of applications andmanagement systems pose many new problems. The databases of the traditional grid and the variousinitiatives related to new technologies have given rise to many different management systems with seve...

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Detalles Bibliográficos
Autores: Guerrero Alonso, Juan Ignacio, García Delgado, Antonio, Personal Vázquez, Enrique, Luque Rodríguez, Joaquín, León de Mora, Carlos
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2017
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/77082
Acceso en línea:https://hdl.handle.net/11441/77082
https://doi.org/10.1016/j.eswa.2017.03.007
Access Level:acceso abierto
Palabra clave:Smart grids
Large-scale integration
Data mining
Standards
Metadata mining
Big data
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spelling Heterogeneous data source integration for smart grid ecosystems based on metadata miningGuerrero Alonso, Juan IgnacioGarcía Delgado, AntonioPersonal Vázquez, EnriqueLuque Rodríguez, JoaquínLeón de Mora, CarlosSmart gridsLarge-scale integrationData miningStandardsMetadata miningBig dataThe arrival of new technologies related to smart grids and the resulting ecosystem of applications andmanagement systems pose many new problems. The databases of the traditional grid and the variousinitiatives related to new technologies have given rise to many different management systems with several formats and different architectures. A heterogeneous data source integration system is necessary toupdate these systems for the new smart grid reality. Additionally, it is necessary to take advantage of theinformation smart grids provide. In this paper, the authors propose a heterogeneous data source integration based on IEC standards and metadata mining. Additionally, an automatic data mining framework isapplied to model the integrated information.Ministerio de Economía y Competitividad TEC2013-40767-RPremio Mensual Publicación Científica Destacada de la US. Escuela Politécnica SuperiorElsevierTecnología ElectrónicaMinisterio de Economía y Competitividad (MINECO). España2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/77082https://doi.org/10.1016/j.eswa.2017.03.007reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésExpert Systems with Applications, 79 (August 2017), 254-268.TEC2013-40767-Rhttps://www.sciencedirect.com/science/article/pii/S0957417417301501info:eu-repo/semantics/openAccessoai:idus.us.es:11441/770822026-06-17T12:51:07Z
dc.title.none.fl_str_mv Heterogeneous data source integration for smart grid ecosystems based on metadata mining
title Heterogeneous data source integration for smart grid ecosystems based on metadata mining
spellingShingle Heterogeneous data source integration for smart grid ecosystems based on metadata mining
Guerrero Alonso, Juan Ignacio
Smart grids
Large-scale integration
Data mining
Standards
Metadata mining
Big data
title_short Heterogeneous data source integration for smart grid ecosystems based on metadata mining
title_full Heterogeneous data source integration for smart grid ecosystems based on metadata mining
title_fullStr Heterogeneous data source integration for smart grid ecosystems based on metadata mining
title_full_unstemmed Heterogeneous data source integration for smart grid ecosystems based on metadata mining
title_sort Heterogeneous data source integration for smart grid ecosystems based on metadata mining
dc.creator.none.fl_str_mv Guerrero Alonso, Juan Ignacio
García Delgado, Antonio
Personal Vázquez, Enrique
Luque Rodríguez, Joaquín
León de Mora, Carlos
author Guerrero Alonso, Juan Ignacio
author_facet Guerrero Alonso, Juan Ignacio
García Delgado, Antonio
Personal Vázquez, Enrique
Luque Rodríguez, Joaquín
León de Mora, Carlos
author_role author
author2 García Delgado, Antonio
Personal Vázquez, Enrique
Luque Rodríguez, Joaquín
León de Mora, Carlos
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Tecnología Electrónica
Ministerio de Economía y Competitividad (MINECO). España
dc.subject.none.fl_str_mv Smart grids
Large-scale integration
Data mining
Standards
Metadata mining
Big data
topic Smart grids
Large-scale integration
Data mining
Standards
Metadata mining
Big data
description The arrival of new technologies related to smart grids and the resulting ecosystem of applications andmanagement systems pose many new problems. The databases of the traditional grid and the variousinitiatives related to new technologies have given rise to many different management systems with several formats and different architectures. A heterogeneous data source integration system is necessary toupdate these systems for the new smart grid reality. Additionally, it is necessary to take advantage of theinformation smart grids provide. In this paper, the authors propose a heterogeneous data source integration based on IEC standards and metadata mining. Additionally, an automatic data mining framework isapplied to model the integrated information.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/77082
https://doi.org/10.1016/j.eswa.2017.03.007
url https://hdl.handle.net/11441/77082
https://doi.org/10.1016/j.eswa.2017.03.007
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Expert Systems with Applications, 79 (August 2017), 254-268.
TEC2013-40767-R
https://www.sciencedirect.com/science/article/pii/S0957417417301501
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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