PARALLEL AND DISTRIBUTED KNOWLEDGE DISCOVERY ON THE GRID: A REFERENCE ARCHITECTURE
Mario Cannataro, Domenico Talia · 2000
In an increasing number of scientific and commercial areas, tools and systems for the analysis of large data sets are emerging as important resources. In particular, when large data sets are coupled with geographic distribution of data, users and systems, it is necessary to combine different technologies for implementing high-performance distributed knowledge discovery systems. The discipline that study and use those tools is named Parallel and Distributed Knowledge Discovery (PDKD). In this paper we introduce a reference software architecture for PDKD systems that is built on top of computational grids that provide dependable, consistent, and pervasive access to high-end computational resources. The proposed architecture uses the grid services and defines a set of additional layers to implement the services of distributed knowledge discovery process on distributed computers where each node can be a sequential or a parallel machine. 1