Knowledge Discovery in Databases (KDD) employs a variety of software systems and tools, collectively called data mining, to find useful patterns, models and trends in large volumes of data. In many scientific and commercial applications, it is necessary to perform the analysis of large data sets, maintained over geographically distributed sites, by using the computational power of distributed and parallel systems. These techniques are investigated in the domain of Parallel and Distributed Knowledge Discovery (PDKD). In this area grid technologies may play a significant role in providing an effective computational support for knowledge discovery applications.

We designed a software architecture for geographically distributed PDKD applications called KNOWLEDGE GRID, which is designed on top of computational grid mechanisms, provided by grid environments such as Globus. The Knowledge Grid uses the basic grid services such as communication, authentication, information, and resource management to build more specific PDKD tools and services. The Knowledge Grid services are organized into two layers: core K-grid layer, which is built on top of generic grid services, and high level K-grid layer, which is implemented over the core layer.

The KNOWLEDGE GRID enables the collaboration of scientists that must mine data that are stored in different research centers as well as executive managers that must use a knowledge management system that operates on several data warehouses located in the different company establishments.

A short abstract that outlines the KNOWLEDGE GRID can be found here.

Additional information and publications about the KNOWLEDGE GRID can be found in the KNOWLEDGE GRID site.