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Volume: 5 Issue: 1

An Efficient Knowledge Management Tool for Distributed Data Mining Environments

Nhien-An Le-Khac, Lamine M. Aouad, M-Tahar Kechadi

Abstract:
Today, a deluge of data is collected from different fields. These massive amounts of data, which are often geographically distributed and owned by different organisations are being mined. As consequence, a large amount of knowledge is being produced. This causes the problem of efficient knowledge management in distributed data mining (DDM). The main aim of (DDM) is to exploit fully the benefit of distributed data analysis while minimising the communication overhead. Existing (DDM) techniques perform partial analysis on local data at individual sites and then generate global models by aggregating them. These two steps are not independent since naive approaches to local analysis may produce incorrect and ambiguous global data models. To overcome this problem, we present a tool called ""knowledge map"" to easily and efficiently represent knowledge built by a mining process on large-scale distributed platforms such as Grids. This will also facilitate the integration/coordination of local mining processes and existing knowledge to increase the accuracy of the final models. This approach is being tested on very large datasets.

Keywords:
knowledge map, distributed data mining, knowledge management, knowledge representative, meta-knowledge, rule net

doi:10.5019/j.ijcir.2004.165

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