Application Resource Requirement Estimation in a Parallel-Pipeline Model of Execution
IEEE Transactions on Parallel and Distributed Systems
A new unsupervised feature selection method for text clustering based on genetic algorithms
Journal of Intelligent Information Systems
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Abstract: One of the primary tasks in mining distributed textual data is feature extraction. The widespread digitization of information has created a wealth of data that requires novel approaches to feature extraction in a distributed environment. We propose a massively parallel model for feature extraction that employs unused cycles on networks of PCs/workstations in a highly distributed environment. We have developed an analytical model of the time and communication complexity of the feature extraction process in this environment based on feature extraction algorithms developed in our textual data mining research with HDDI(tm) [1] [18] [20]. We show that speedups linear in the number of processors are achievable for applications involving reduction operations based on a novel, parallel pipelined model of execution. We are in the process of validating our analytical model with empirical observations based on the extraction of features from a large number of pages on the World Wide Web.