Using Grids for Distributed Knowledge Discovery

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Author(s): Antonio Congiusta (DEIS—University of Calabria, Italy), Domenico Talia (DEIS—University of Calabria, Italy)and Paolo Trunfio (DEIS—University of Calabria, Italy)
Copyright: 2008
Pages: 15
Source title: Mathematical Methods for Knowledge Discovery and Data Mining
Source Author(s)/Editor(s): Giovanni Felici (Consiglio Nazionale delle Richerche, Italy)and Carlo Vercellis (Politecnico di Milano, Italy)
DOI: 10.4018/978-1-59904-528-3.ch017

Keywords: Data Mining and Databases / Information Science Reference / Knowledge Discovery / Library & Information Science

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Abstract

Knowledge discovery is a compute and data intensive process that allows for finding patterns, trends, and models in large datasets. The Grid can be effectively exploited for deploying knowledge discovery applications because of the high-performance it can offer and its distributed infrastructure. For effective use of Grids in knowledge discovery, the development of middleware is critical to support data management, data transfer, data mining and knowledge representation. To such purpose, we designed the Knowledge Grid, a high-level environment providing for Grid-based knowledge discovery tools and services. Such services allow users to create and manage complex knowledge discovery applications, composed as workflows that integrate data sources and data mining tools provided as distributed Grid services. This chapter describes the Knowledge Grid architecture and describes how its components can be used to design and implement distributed knowledge discovery applications. Then, the chapter describes how the Knowledge Grid services can be made accessible using the Open Grid Services Architecture (OGSA) model.

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