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Registro Completo |
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Biblioteca(s): |
Embrapa Caprinos e Ovinos. |
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Data corrente: |
29/10/2012 |
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Data da última atualização: |
17/05/2022 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
BATISTA, N. J. M.; BONFIM, J. M.; FERNANDES, F. E. P.; TONUCCI, R. G.; SOUZA, H. A. de; ROGERIO, M. C. P. |
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Afiliação: |
Nielyson Junio Marcos Batista, Graduação - Universidade Estadual Vale do Acaraú (UVA) - Sobral, CE; Joice Melo Bonfim, Graduação - UVA - Sobral, CE; FRANCISCO EDEN PAIVA FERNANDES, CNPC; RAFAEL GONCALVES TONUCCI, CNPC; HENRIQUE ANTUNES DE SOUZA, CNPC; MARCOS CLAUDIO PINHEIRO ROGERIO, CNPC. |
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Título: |
Desempenho de cordeiros de diferentes grupos genéticos alimentados com silagem de sorgo, oriundo de área adubada com nitrogênio. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
In: ENCONTRO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA CAPRINOS E OVINOS, 1., 2012, Sobral. Resumos... Sobral: Embrapa Caprinos e Ovinos, 2012. p. 35-36. (Embrapa Caprinos e Ovinos. Documentos, 104). |
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Idioma: |
Português |
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Palavras-Chave: |
Alimentação animal; Desempenho animal. |
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Thesagro: |
Nutrição animal; Ovino; Sorghum bicolor; Sorgo forrageiro. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/68953/1/RAC-Desempenho-de-cordeiros.pdf
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Marc: |
LEADER 00900nam a2200229 a 4500
001 1938420
005 2022-05-17
008 2012 bl uuuu u00u1 u #d
100 1 $aBATISTA, N. J. M.
245 $aDesempenho de cordeiros de diferentes grupos genéticos alimentados com silagem de sorgo, oriundo de área adubada com nitrogênio.$h[electronic resource]
260 $aIn: ENCONTRO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA CAPRINOS E OVINOS, 1., 2012, Sobral. Resumos... Sobral: Embrapa Caprinos e Ovinos, 2012. p. 35-36. (Embrapa Caprinos e Ovinos. Documentos, 104).$c2012
650 $aNutrição animal
650 $aOvino
650 $aSorghum bicolor
650 $aSorgo forrageiro
653 $aAlimentação animal
653 $aDesempenho animal
700 1 $aBONFIM, J. M.
700 1 $aFERNANDES, F. E. P.
700 1 $aTONUCCI, R. G.
700 1 $aSOUZA, H. A. de
700 1 $aROGERIO, M. C. P.
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Registro original: |
Embrapa Caprinos e Ovinos (CNPC) |
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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: |
18/09/2008 |
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Data da última atualização: |
16/01/2020 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
OLIVEIRA, S. R. de M. |
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Afiliação: |
STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA. |
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Título: |
Privacy-preserving data mining. |
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Ano de publicação: |
2009 |
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Fonte/Imprenta: |
In: WANG, J. Encyclopedia of data warehousing and mining. 2nd ed. Hershey: Information Science Reference, 2009. |
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Volume: |
v. III |
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Páginas: |
p. 1582-1588. |
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ISBN: |
978-1-60566-011-0 |
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Idioma: |
Inglês |
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Notas: |
Na publicação: Stanley R. M. Oliveira. |
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Conteúdo: |
Despite its benefits in various areas (e.g., business, medical analysis, scientific data analysis, etc), the use of data mining techniques can also result in new threats to privacy and information security. The problem is not data mining itself, but the way data mining is done. "Data mining results rarely violate privacy, as they generally reveal high-Ievel knowledge rather than disclosing instances of data" (Vaidya & Clifton, 2003). However, the concern among privacy advocates is well founded, as bringing data together to support data mining projects makes misuse easier. Thus, in the absence ofadequate safeguards, the use of data mining can jeopardize the privacy and autonomy of individuals. Privacy-preserving data mining (PPDM) cannot simply be addressed by restricting data collection or even by restricting the secondary use of information technology (Brankovic & V. Estivill-Castro, 1999). Moreover, there is no exact solution that resolves privacy preservation in data mining. In some applications, solutions for PPDM problems might meet privacy requirements and provide valid data mining results (Oliveira & ZaYane, 2004b). We have witnessed three major landmarks that characterize the progress and success of this new research area: the conceptive landmark, the deployment landmark, and the prospective landmark. The Conceptive landmark characterizes the period in which central figures in the community, such as O'Leary (1995), Piatetsky-Shapiro (1995), and others (Klõsgen, 1995; Clifton & Marks, 1996), investigated the success of knowledge discovery and some of the important areas where it can conflict with privacy concerns. The key finding was that knowledge discovery can open new threats to informational privacy and information security if not done or used properly. The Deployment landmark is the current period in which an increasing number of PPDM techniques have been developed and have been published in refereed conferences. The information available today is spread over countless papers and conference proceedings. The results achieved in the last years are promising and suggest that PPDM will achieve the goals that have been set for it. The Prospective landmark is a new period in which directed efforts toward standardization occur. At this stage, there is no consensus on privacy principles, policies, and requirements as a foundation for the development and deployment of new PPDM techniques. The excessive number of techniques is leading to confusion among developers, practitioners, and others interested in this technology. One of the most important challenges in PPDM now is to establish the groundwork for further research and development in this area. MenosDespite its benefits in various areas (e.g., business, medical analysis, scientific data analysis, etc), the use of data mining techniques can also result in new threats to privacy and information security. The problem is not data mining itself, but the way data mining is done. "Data mining results rarely violate privacy, as they generally reveal high-Ievel knowledge rather than disclosing instances of data" (Vaidya & Clifton, 2003). However, the concern among privacy advocates is well founded, as bringing data together to support data mining projects makes misuse easier. Thus, in the absence ofadequate safeguards, the use of data mining can jeopardize the privacy and autonomy of individuals. Privacy-preserving data mining (PPDM) cannot simply be addressed by restricting data collection or even by restricting the secondary use of information technology (Brankovic & V. Estivill-Castro, 1999). Moreover, there is no exact solution that resolves privacy preservation in data mining. In some applications, solutions for PPDM problems might meet privacy requirements and provide valid data mining results (Oliveira & ZaYane, 2004b). We have witnessed three major landmarks that characterize the progress and success of this new research area: the conceptive landmark, the deployment landmark, and the prospective landmark. The Conceptive landmark characterizes the period in which central figures in the community, such as O'Leary (1995), Piatetsky-Shapiro (1995), and others (Klõsgen, 1995;... Mostrar Tudo |
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Palavras-Chave: |
Data mining; Mineração de dados; Preservação da informação; Privacidade; Privacy; Segurança. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 03438naa a2200241 a 4500
001 1008652
005 2020-01-16
008 2009 bl uuuu u00u1 u #d
020 $a978-1-60566-011-0
100 1 $aOLIVEIRA, S. R. de M.
245 $aPrivacy-preserving data mining.$h[electronic resource]
260 $c2009
300 $ap. 1582-1588. v. III
490 $vv. III
500 $aNa publicação: Stanley R. M. Oliveira.
520 $aDespite its benefits in various areas (e.g., business, medical analysis, scientific data analysis, etc), the use of data mining techniques can also result in new threats to privacy and information security. The problem is not data mining itself, but the way data mining is done. "Data mining results rarely violate privacy, as they generally reveal high-Ievel knowledge rather than disclosing instances of data" (Vaidya & Clifton, 2003). However, the concern among privacy advocates is well founded, as bringing data together to support data mining projects makes misuse easier. Thus, in the absence ofadequate safeguards, the use of data mining can jeopardize the privacy and autonomy of individuals. Privacy-preserving data mining (PPDM) cannot simply be addressed by restricting data collection or even by restricting the secondary use of information technology (Brankovic & V. Estivill-Castro, 1999). Moreover, there is no exact solution that resolves privacy preservation in data mining. In some applications, solutions for PPDM problems might meet privacy requirements and provide valid data mining results (Oliveira & ZaYane, 2004b). We have witnessed three major landmarks that characterize the progress and success of this new research area: the conceptive landmark, the deployment landmark, and the prospective landmark. The Conceptive landmark characterizes the period in which central figures in the community, such as O'Leary (1995), Piatetsky-Shapiro (1995), and others (Klõsgen, 1995; Clifton & Marks, 1996), investigated the success of knowledge discovery and some of the important areas where it can conflict with privacy concerns. The key finding was that knowledge discovery can open new threats to informational privacy and information security if not done or used properly. The Deployment landmark is the current period in which an increasing number of PPDM techniques have been developed and have been published in refereed conferences. The information available today is spread over countless papers and conference proceedings. The results achieved in the last years are promising and suggest that PPDM will achieve the goals that have been set for it. The Prospective landmark is a new period in which directed efforts toward standardization occur. At this stage, there is no consensus on privacy principles, policies, and requirements as a foundation for the development and deployment of new PPDM techniques. The excessive number of techniques is leading to confusion among developers, practitioners, and others interested in this technology. One of the most important challenges in PPDM now is to establish the groundwork for further research and development in this area.
653 $aData mining
653 $aMineração de dados
653 $aPreservação da informação
653 $aPrivacidade
653 $aPrivacy
653 $aSegurança
773 $tIn: WANG, J. Encyclopedia of data warehousing and mining. 2nd ed. Hershey: Information Science Reference, 2009.
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