Web text categorization on GBODSS

Grid technology has the potential to improve the accessibility of digital libraries. The participants in Project GBODSS (grid-based open DSS) are in the process of developing a new open decision support system framework based on grid technologies. Automated text categorization has been extensively studied and various techniques for document categorization based on machine learning approaches have been proposed. However, most of these experimental prototypes, for the purpose of evaluating different techniques, have been restricted to the heterogeneous, autonomic, dynamic and distributed Internet environment. In this study, an approach based on support vector machines (SVMs) for Web text mining of large-scale systems on GBODSS is developed to support enterprise decision making. The experiments were conducted on different configurations of grid network and computation time was recorded for each operation. We analyzed our result with various grid configurations and it shows speed up of computation time is almost super linear.