Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/256494
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dc.contributor.authorROMANI, L. A. S.pt_BR
dc.contributor.authorTRAINA, A. J. M.pt_BR
dc.contributor.authorSOUSA, E. P. M. dept_BR
dc.contributor.authorZULLO JÚNIOR, J.pt_BR
dc.contributor.authorAVILA, A. M. H.pt_BR
dc.contributor.authorRODRIGUES JR. J. F.pt_BR
dc.contributor.authorTRAINA JÚNIOR. C.pt_BR
dc.date.accessioned2011-04-09T17:37:24Z-
dc.date.available2011-04-09T17:37:24Z-
dc.date.created2009-08-06pt_BR
dc.date.issued2009pt_BR
dc.identifier.citationIn: CONGRESSO DA SOCIEDADE BRASILEIRA DE COMPUTAÇÃO, 29., 2009, Bento Gonçalves. Anais... Rio Grande do SUL: Instituto de Informática UFRGS.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/256494pt_BR
dc.descriptionIn the past few years, improvements in the data acquisition technology have decreased the time interval of data gathering. Consequently, institutions have stored huge amounts of data such as climate time series and remote sensing images. Computational models to filter, transform, merge and analyze data from many different areas are complex and challenging. The complexity increases even more when combining several knowledge domains. Examples are research in climatic changes, biofuel production and environmental problems. A possible solution to the problem is the association of several computational techniques. Accordingly, this paper presents a framework to analyze, monitor and visualize climate and remote sensing data by employing methods based on fractal theory, data mining and visualization techniques. Initial experiments showed that the information and knowledge discovered from this framework can be employed to monitor sugar cane crops, helping agricultural entrepreneurs to make decisions in order to become more productive. Sugar cane is the main source to ethanol production in Brazil, and has a strategic importance for the country economy and to guarantee the Brazilian self-sufficiency in this important, renewable source of energy.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectDados agrometeorológicospt_BR
dc.subjectDados de sensoriamento remotopt_BR
dc.subjectDados climáticospt_BR
dc.subjectTeoria dos fractaispt_BR
dc.subjectMineração de dadospt_BR
dc.subjectTécnicas de visualizaçãopt_BR
dc.subjectDados massivospt_BR
dc.subjectSéries temporaispt_BR
dc.subjectCana-de-açúcarpt_BR
dc.subjectData miningpt_BR
dc.titleComputational framework to analyze agrometeorological, climate and remote sensing data: challenges and perspectives.pt_BR
dc.typeArtigo em anais e proceedingspt_BR
dc.date.updated2020-01-31T11:11:11Zpt_BR
dc.subject.thesagroAgriculturapt_BR
dc.subject.nalthesaurusRemote sensingpt_BR
dc.subject.nalthesaurusAgriculturept_BR
dc.subject.nalthesaurusSugarcaneeng
dc.description.notesCSBC 2009.pt_BR
dc.format.extent2p. 323-337.pt_BR
riaa.ainfo.id256494pt_BR
riaa.ainfo.lastupdate2020-01-31 -02:00:00pt_BR
dc.contributor.institutionLUCIANA ALVIM SANTOS ROMANI, CNPTIA; AGMA J. M. TRAINA, Ciência da Computação/USP São Carlos; ELAINE P. M. DE SOUSA, Ciência da Computação/USP São Carlos; JURANDIR ZULLO JÚNIOR, CEPAGRI/ UNICAMP; ANA M. H. AVILA, CEPAGRI/UNICAMP; JOSE FERNANDO RODRIGUES JR., UFSCAR; CAETANO TRAINA JÚNIOR, Ciência da Computação/USP São Carlos.pt_BR
Aparece nas coleções:Artigo em anais de congresso (CNPTIA)

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