Conference Paper

Handling Large Volumes of Mined Knowledge with a Self-Reconfigurable Topology on Distributed Systems.

Sch. of Comput. Sci. & Inf., Univ. Coll. Dublin, Dublin, Ireland
DOI: 10.1109/ICMLA.2008.30 Conference: Seventh International Conference on Machine Learning and Applications, ICMLA 2008, San Diego, California, USA, 11-13 December 2008
Source: DBLP


Nowadays, massive amounts of data which are often geographically distributed and owned by different organisations, are being mined. As consequence, large volumes of knowledge is being generated. This causes the problem of efficient knowledge management in distributed data mining (DDM). The main aim of is to exploit fully the benefit of distributed data analysis while minimising the communication overhead. Existing DDM techniques perform partial analysis of local data at individual sites and then generate global models by aggregating the local results. 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 introduce a distributed knowledge map based on an efficient self-reconfiguration network topology to represent easily and exploit efficiently the knowledge mined in large scale distributed platforms. This will also facilitate the integration/coordination of local mining processes and existing knowledge to build global models. In this paper, we implement this knowledge map and present some preliminary results about its performance.

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