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

Abstract

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.