A data mining toolset for distributed high-performance platforms

Today a large number of scientific and commercial applications often require to analyse large data sets maintained over geographically distributed sites by using the computational power of distributed high-performance environments. Advances in networking technology and computational infrastructure made it possible to construct large-scale distributed computing platforms, called computational grids, that provide dependable, consistent, and pervasive access to high-end computational resources. Grids can play a significant role in providing an effective computational support for distributed data mining applications. Currently we are developing a software system for geographically distributed knowledge discovery applications called KNOWLEDGE GRID, which is designed on top of computational grid mechanisms, provided by grid environments such as Globus. In this paper we present an integrated toolset named VEGA (Visual Environment for Grid Applications), which allows a Knowledge Grid user to develop and execute distributed data mining computations in a simple and effective way.