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Published Articles >> Table of Contents >> Abstract
International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2
p. 480
Confidence on Approximate Query in Large Datasets
Charles Wesley Ford, University of Arkansas at Little Rock
Chia-Chu Chiang, University of Arkansas at Little Rock
Hao Wu, University of Arkansas at Little Rock
Radhika R. Chilka, University of Arkansas at Little Rock
John Talburt, Acxiom Corporation, Little Rock
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ITCC.2004.1286700
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| Abstract |
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The evolution of the World Wide Web has brought us
enormous amounts of information for business and research
use. Design and implementation of an automated
system for web data mining has become important for companies
wishing to utilize useful information from the web.
This paper is an attempt at describing confidence on approximate
queries on large datasets which is done in the
context of an automated system for web data mining. The
system has been designed to identify, extract, filter, and
analyze data from web resources. An approach to evaluating
the quality of extracted web data is also discussed. This
work is an exploratory study of web data retrieval and web
data analysis.
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 Additional Information
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Citation:
Charles Wesley Ford, Chia-Chu Chiang, Hao Wu, Radhika R. Chilka, John Talburt,
"Confidence on Approximate Query in Large Datasets,"
itcc,
p. 480,
International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2,
2004
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