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Registro Completo |
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Biblioteca(s): |
Embrapa Clima Temperado. |
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Data corrente: |
05/01/2008 |
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Data da última atualização: |
15/08/2008 |
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Tipo da produção científica: |
Artigo em Anais de Congresso / Nota Técnica |
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Autoria: |
GONÇALVES, L. C.; PILLON, C. N.; SANTOS, D. C. dos; LIMA, C. L. R. de L.; SILVA, S. D. dos A. e.; SANDRINI, W. C. |
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Título: |
Carbono da biomassa microbiana de um argissolo sob plantio direto de milho e de mamona. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
In: SIMPÓSIO ESTADUAL DE AGROENERGIA, 1.; REUNIÃO TÉCNICA ANUAL DE AGROENERGIA-RS, 1., 2007, Pelotas. Anais... Pelotas: Embrapa Clima Temperado, 2007. |
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Volume: |
1 CD-ROM. |
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Idioma: |
Português |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00675naa a2200193 a 4500
001 1745392
005 2008-08-15
008 2007 bl uuuu u00u1 u #d
100 1 $aGONÇALVES, L. C.
245 $aCarbono da biomassa microbiana de um argissolo sob plantio direto de milho e de mamona.
260 $c2007
300 $a1 CD-ROM.
490 $v1 CD-ROM.
700 1 $aPILLON, C. N.
700 1 $aSANTOS, D. C. dos
700 1 $aLIMA, C. L. R. de L.
700 1 $aSILVA, S. D. dos A. e.
700 1 $aSANDRINI, W. C.
773 $tIn: SIMPÓSIO ESTADUAL DE AGROENERGIA, 1.; REUNIÃO TÉCNICA ANUAL DE AGROENERGIA-RS, 1., 2007, Pelotas. Anais... Pelotas: Embrapa Clima Temperado, 2007.
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Embrapa Clima Temperado (CPACT) |
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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. |
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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Embrapa Agricultura Digital (CNPTIA) |
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