Design and Development of Data Mining on Computational Grids in Grid Miner
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Abstract
Data mining algorithms and knowledge discovery processes are both compute and data intensive, therefore the Grid can offer a
computing and data management infrastructure for supporting decentralized and parallel data analysis. Distribution of data and computation
allows for solving larger problems and execute applications that are distributed in nature. The Grid is a distributed computing infrastructure that
enables coordinated resource sharing within dynamic organizations consisting of individuals, institutions, and resources. The Grid extends the
distributed and parallel computing paradigms allowing resource negotiation and dynamical allocation, heterogeneity, open protocols and
services. Grid environments can be used both for compute intensive tasks and data intensive applications as they offer resources, services, and
data access mechanisms. Grid-based data mining uses Grids as decentralized high-performance platforms where to execute data mining tasks
and knowledge discovery algorithms and applications. Here we outline the research activities, challenges in the Grid based mining and sketch
the promising future directions for developing Grid based distributed data mining. This paper discusses how Grid computing can be used to
support distributed data mining.
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Keywords: Grid Computing; Distributed Mining; Parallel Computing; Data Analysis; Algorithms.
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