Towards IoT data classification through semantic features

Detalhes bibliográficos
Autor(a) principal: Antunes, M.
Data de Publicação: 2018
Outros Autores: Gomes, Diogo Nuno, Aguiar, R. L.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/21424
Resumo: The technological world has grown by incorporating billions of small sensing devices, collecting and sharing huge amounts of diversified data. As the number of such devices grows, it becomes increasingly difficult to manage all these new data sources. Currently there is no uniform way to represent, share, and understand IoT data, leading to information silos that hinder the realization of complex IoT/M2M scenarios. IoT/M2M scenarios will only achieve their full potential when the devices work and learn together with minimal human intervention. In this paper we discuss the limitations of current storage and analytical solutions, point the advantages of semantic approaches for context organization and extend our unsupervised model to learn word categories automatically. Our solution was evaluated against Miller-Charles dataset and a IoT semantic dataset extracted from a popular IoT platform, achieving a correlation of 0.63.
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spelling Towards IoT data classification through semantic featuresIoTSemantic similarityContext informationM2MThe technological world has grown by incorporating billions of small sensing devices, collecting and sharing huge amounts of diversified data. As the number of such devices grows, it becomes increasingly difficult to manage all these new data sources. Currently there is no uniform way to represent, share, and understand IoT data, leading to information silos that hinder the realization of complex IoT/M2M scenarios. IoT/M2M scenarios will only achieve their full potential when the devices work and learn together with minimal human intervention. In this paper we discuss the limitations of current storage and analytical solutions, point the advantages of semantic approaches for context organization and extend our unsupervised model to learn word categories automatically. Our solution was evaluated against Miller-Charles dataset and a IoT semantic dataset extracted from a popular IoT platform, achieving a correlation of 0.63.Elsevier2018-01-12T16:41:03Z2018-01-01T00:00:00Z2018info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/21424eng0167-739X10.1016/j.future.2017.11.045Antunes, M.Gomes, Diogo NunoAguiar, R. L.info: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:RCAAP2024-02-22T11:37:08Zoai:ria.ua.pt:10773/21424Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:53:57.050811Repositório 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 Towards IoT data classification through semantic features
title Towards IoT data classification through semantic features
spellingShingle Towards IoT data classification through semantic features
Antunes, M.
IoT
Semantic similarity
Context information
M2M
title_short Towards IoT data classification through semantic features
title_full Towards IoT data classification through semantic features
title_fullStr Towards IoT data classification through semantic features
title_full_unstemmed Towards IoT data classification through semantic features
title_sort Towards IoT data classification through semantic features
author Antunes, M.
author_facet Antunes, M.
Gomes, Diogo Nuno
Aguiar, R. L.
author_role author
author2 Gomes, Diogo Nuno
Aguiar, R. L.
author2_role author
author
dc.contributor.author.fl_str_mv Antunes, M.
Gomes, Diogo Nuno
Aguiar, R. L.
dc.subject.por.fl_str_mv IoT
Semantic similarity
Context information
M2M
topic IoT
Semantic similarity
Context information
M2M
description The technological world has grown by incorporating billions of small sensing devices, collecting and sharing huge amounts of diversified data. As the number of such devices grows, it becomes increasingly difficult to manage all these new data sources. Currently there is no uniform way to represent, share, and understand IoT data, leading to information silos that hinder the realization of complex IoT/M2M scenarios. IoT/M2M scenarios will only achieve their full potential when the devices work and learn together with minimal human intervention. In this paper we discuss the limitations of current storage and analytical solutions, point the advantages of semantic approaches for context organization and extend our unsupervised model to learn word categories automatically. Our solution was evaluated against Miller-Charles dataset and a IoT semantic dataset extracted from a popular IoT platform, achieving a correlation of 0.63.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-12T16:41:03Z
2018-01-01T00:00:00Z
2018
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/10773/21424
url http://hdl.handle.net/10773/21424
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0167-739X
10.1016/j.future.2017.11.045
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 Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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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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