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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
04/12/2014 |
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Data da última atualização: |
08/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
CHINO, D. Y. T.; GONÇALVES, R. R. V.; ROMANI, L. A. S.; TRAINA JÚNIOR, C.; TRAINA, A. J. M. |
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Afiliação: |
DANIEL Y. T. CHINO, ICMC/USP; RENATA R. V. GONÇALVES, Cepagri/Unicamp; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; CAETANO TRAINA JÚNIOR, ICMC/USP; AGMA J. M. TRAINA, ICMC/USP. |
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Título: |
TrieMotif: a new and efficient method to mine frequent K-motifs from large time series. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, 16.; INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL APPROACHES TO SOFTWARE ENGINEERING, 9., 2014, Lisbon. Proceedings... [S.l.]: Scitepress, 2014. |
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Páginas: |
p. 60-69. |
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ISBN: |
978-989-758-027-7 |
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Idioma: |
Inglês |
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Notas: |
ICEIS 2014. |
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Conteúdo: |
Abstract: Finding previously unknown patterns that frequently occur on time series is a core task of mining time series. These patterns are known as time series motifs and are essential to associate events and meaningful occurrences within the time series. In this work we propose a method based on a trie data structure, that allows a fast and accurate time series motif discovery. From the experiments performed on synthetic and real data we can see that our TrieMotif approach is able to efficiently find motifs even when the size of the time series goes longer, being in average 3 times faster and requiring 10 times less memory than the state of the art approach. As a case study on real data, we also evaluated our method using time series extracted from remote sensing images regarding sugarcane crops. Our proposed method was able to find relevant patterns, as sugarcane cycles and other land covers inside the same area. |
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Palavras-Chave: |
AVHRR-NOAA images; Imagens AVHRR-NOAA; Imagens de satélite; Séries temporais. |
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Thesagro: |
Sensoriamento Remoto. |
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Thesaurus Nal: |
Remote sensing; Time series analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01925nam a2200277 a 4500
001 2001713
005 2020-01-08
008 2014 bl uuuu u00u1 u #d
020 $a978-989-758-027-7
100 1 $aCHINO, D. Y. T.
245 $aTrieMotif$ba new and efficient method to mine frequent K-motifs from large time series.$h[electronic resource]
260 $aIn: INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, 16.; INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL APPROACHES TO SOFTWARE ENGINEERING, 9., 2014, Lisbon. Proceedings... [S.l.]: Scitepress$c2014
300 $ap. 60-69.
500 $aICEIS 2014.
520 $aAbstract: Finding previously unknown patterns that frequently occur on time series is a core task of mining time series. These patterns are known as time series motifs and are essential to associate events and meaningful occurrences within the time series. In this work we propose a method based on a trie data structure, that allows a fast and accurate time series motif discovery. From the experiments performed on synthetic and real data we can see that our TrieMotif approach is able to efficiently find motifs even when the size of the time series goes longer, being in average 3 times faster and requiring 10 times less memory than the state of the art approach. As a case study on real data, we also evaluated our method using time series extracted from remote sensing images regarding sugarcane crops. Our proposed method was able to find relevant patterns, as sugarcane cycles and other land covers inside the same area.
650 $aRemote sensing
650 $aTime series analysis
650 $aSensoriamento Remoto
653 $aAVHRR-NOAA images
653 $aImagens AVHRR-NOAA
653 $aImagens de satélite
653 $aSéries temporais
700 1 $aGONÇALVES, R. R. V.
700 1 $aROMANI, L. A. S.
700 1 $aTRAINA JÚNIOR, C.
700 1 $aTRAINA, A. J. M.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| Registros recuperados : 65 | |
| 9. |  | AMARAL, B. F. do; GONÇALVES, R. R. V.; ROMANI, L. A. S.; SOUSA, E. P. M. Aprimorando a classificação semissupervisionada de séries temporais extraídas de imagens de satélite. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING, 2., 2014, São Carlos, SP. Proceedings... São Carlos, SP: ICMC/USP, 2014. p. 1-8. KDMiLe 2014.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 10. | | HAMADA, E.; GHINI, R.; GONÇALVES, R. R. V.; PEREIRA, D. A. Avaliação do efeito de mudança climática sobre problemas fitossanitários: comparação de métodos de elaboração de mapas. In: CONGRESSO BRASILEIRO DE BIOMETEOROLOGIA, 4., 2006, Ribeirão Preto,SP. [Anais...]. Ribeirão Preto, SP: SBB, IZ, 2006. p. 1-5.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Meio Ambiente. |
|  |
| 11. | | GHINI, R.; HAMADA, E.; GONÇALVES, R. R. V.; GASPAROTTO, L.; PEREIRA, J. C. R. Análise de risco das mudanças climáticas globais sobre a sigatoka-negra da bananeira no Brasil. Fitopatologia Brasileira, Brasília, v.32, n.3, p.197-204, 2007.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: Nacional - A |
| Biblioteca(s): Embrapa Amazônia Ocidental; Embrapa Meio Ambiente. |
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| 18. | | GONÇALVES, R. R. V.; NASCIMENTO, C. R.; J. ZULLO JÚNIOR; ROMANI, L. A. S. Relationship between the spectral response of sugar cane, based on AVHRR/NOAA satellite images, and the climate condition, in the state of São Paulo (Brazil), from 2001 to 2008. In: INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTI-TEMPORAL REMOTE SENSING IMAGES, 5., 2009, Groton, Connecticut. Proceedings... Storrs: UConn, 2009. p. 315-322. MultiTemp 2009.| Tipo: Artigo em Anais de Congresso / Nota Técnica |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 65 | |
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