Modelling patterns in continuous streams of data
Autor(a) principal: | |
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Data de Publicação: | 2017 |
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/10773/21336 |
Resumo: | The untapped source of information, extracted from the increasing number of sensors, can be explored to improve and optimize several systems. Yet, hand in hand with this growth goes the increasing difficulty to manage and organize all this new information. The lack of a standard context representation scheme is one of the main struggles in this research area, conventional methods for extracting knowledge from data rely on a standard representation or a priori relation. Which may not be feasible for IoT and M2M scenarios, with this in mind we propose a stream characterization model which aims to provide the foundations for a novel stream similarity metric. Complementing previous work on context organization, we aim to provide an automatic stream organizational model without enforcing specific representations. In this paper we extend our work on stream characterization and devise a novel similarity method |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Modelling patterns in continuous streams of dataStream MiningTime SeriesMachine LearningIoTM2MThe untapped source of information, extracted from the increasing number of sensors, can be explored to improve and optimize several systems. Yet, hand in hand with this growth goes the increasing difficulty to manage and organize all this new information. The lack of a standard context representation scheme is one of the main struggles in this research area, conventional methods for extracting knowledge from data rely on a standard representation or a priori relation. Which may not be feasible for IoT and M2M scenarios, with this in mind we propose a stream characterization model which aims to provide the foundations for a novel stream similarity metric. Complementing previous work on context organization, we aim to provide an automatic stream organizational model without enforcing specific representations. In this paper we extend our work on stream characterization and devise a novel similarity methodResearch Online Publishing2018-01-05T12:27:07Z2017-01-01T00:00:00Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/21336eng2365-029XJesus, RAntunes, MGomes, DAguiar, Rinfo: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:10773/21336Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:53:57.006351Repositório 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 |
Modelling patterns in continuous streams of data |
title |
Modelling patterns in continuous streams of data |
spellingShingle |
Modelling patterns in continuous streams of data Jesus, R Stream Mining Time Series Machine Learning IoT M2M |
title_short |
Modelling patterns in continuous streams of data |
title_full |
Modelling patterns in continuous streams of data |
title_fullStr |
Modelling patterns in continuous streams of data |
title_full_unstemmed |
Modelling patterns in continuous streams of data |
title_sort |
Modelling patterns in continuous streams of data |
author |
Jesus, R |
author_facet |
Jesus, R Antunes, M Gomes, D Aguiar, R |
author_role |
author |
author2 |
Antunes, M Gomes, D Aguiar, R |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Jesus, R Antunes, M Gomes, D Aguiar, R |
dc.subject.por.fl_str_mv |
Stream Mining Time Series Machine Learning IoT M2M |
topic |
Stream Mining Time Series Machine Learning IoT M2M |
description |
The untapped source of information, extracted from the increasing number of sensors, can be explored to improve and optimize several systems. Yet, hand in hand with this growth goes the increasing difficulty to manage and organize all this new information. The lack of a standard context representation scheme is one of the main struggles in this research area, conventional methods for extracting knowledge from data rely on a standard representation or a priori relation. Which may not be feasible for IoT and M2M scenarios, with this in mind we propose a stream characterization model which aims to provide the foundations for a novel stream similarity metric. Complementing previous work on context organization, we aim to provide an automatic stream organizational model without enforcing specific representations. In this paper we extend our work on stream characterization and devise a novel similarity method |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01T00:00:00Z 2017 2018-01-05T12:27:07Z |
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/10773/21336 |
url |
http://hdl.handle.net/10773/21336 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2365-029X |
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 |
Research Online Publishing |
publisher.none.fl_str_mv |
Research Online Publishing |
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 instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
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 |
repository.mail.fl_str_mv |
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1799137591400857600 |