Design and Development of Data Mining on Computational Grids in Grid Miner

Data mining algorithms and knowledge discovery processes are both compute and data intensive, therefore the Grid can offer acomputing and data management infrastructure for supporting decentralized and parallel data analysis. Distribution of data and computationallows for solving larger problems and execute applications that are distributed in nature. The Grid is a distributed computing infrastructure thatenables coordinated resource sharing within dynamic organizations consisting of individuals, institutions, and resources. The Grid extends thedistributed and parallel computing paradigms allowing resource negotiation and dynamical allocation, heterogeneity, open protocols andservices. Grid environments can be used both for compute intensive tasks and data intensive applications as they offer resources, services, anddata access mechanisms. Grid-based data mining uses Grids as decentralized high-performance platforms where to execute data mining tasksand knowledge discovery algorithms and applications. Here we outline the research activities, challenges in the Grid based mining and sketchthe promising future directions for developing Grid based distributed data mining. This paper discusses how Grid computing can be used tosupport distributed data mining