Computação paralela para reduzir o tempo de resposta da mineração de dados agrícolas

• Ano de defesa: 2013
• Autor(a) principal: Abreu, Cristian Cosmoski Rangel de
• Orientador(a): Senger, Luciano José
• Banca de defesa: Vaz, Maria Salete Marcon Gomes , Góis, Lourival Aparecido de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: UNIVERSIDADE ESTADUAL DE PONTA GROSSA
• Programa de Pós-Graduação: Programa de Pós Graduação Computação Aplicada
• Departamento: Computação para Tecnologias em Agricultura
• País: BR
• Palavras-chave em Português: computação paralela; mineração de dados; peer-to-peer; tipos de coberturas florestais
• Palavras-chave em Inglês: parallel computing; data mining; peer-to-peer; forest cover types
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tede2.uepg.br/jspui/handle/prefix/162

The objective of this study was investigate the use of parallel computing to reduce the response time of data mining in agriculture. For this purpose, a tool, called Fast Weka been defined and implemented. This tool allows running data mining algorithms and explore parallelism in multi-core computers with the use of threads and distributed systems employing peer-to-peer networks. The exploration of parallelism occurs through the data parallelism inherent to the process of cross-validation (folds). The tool was evaluated through experiments using artificial neural networks data mining algorithms applied to a data set of forest cover types. The multi-thread computing and computing on peer-to-peer networks allowed to reduce the response time of data mining activities. The best results were achieved when employed a multiple number of threads or pairs in the number of folds of cross validation. It was observed and efficiency of 87% when used 4 threads to 24 folds and 86% efficiency also in peer-to-peer networks using 24 folds with 11 pairs.