NDLI: A grid-based architecture for nearest neighbor based condensation of huge datasets

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A grid-based architecture for nearest neighbor based condensation of huge datasets

Content Provider	ACM Digital Library
Author	Folino, Gianluigi Angiulli, Fabrizio
Abstract	Grid computing provides services to the users to discover, transfer, and manipulate large datasets distributed in different locations. Classifying large datasets without using a centralized approach is a key problem in this kind of architectures and, for instance, it is essential for the ever growing datasets bioinformatic scientists face. To this aim, Grid-FCNN, a grid-enabled architecture for classifying huge data set using the nearest neighbor rule is presented in this paper. In order to cope with the communication overhead typical of distributed environments and to reduce memory requirements, two different strategies are presented, namely Grid-FCNN1 and Grid-FCNN2, and their performances in grid environments is analyzed. An analysis of the experimental results, performed on both synthetic and real very large datasets, revealed that these techniques are adapt to be used in a Grid. Furthermore, it is illustrated how the Grid-based algorithm can be applicable in a real bioinformatics scenario.
Starting Page	13
Ending Page	20
Page Count	8
File Format	PDF
ISBN	9781605581552
DOI	10.1145/1384209.1384213
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-06-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Nearest neighbor rule Classification Grid computing Data condensation
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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