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Downloading data from textual deep W...

Yuan, Xiaolei.

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Downloading data from textual deep Web using clustering.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Downloading data from textual deep Web using clustering./
Author:	Yuan, Xiaolei.
Description:	69 p.
Notes:	Source: Masters Abstracts International, Volume: 46-04, page: 2175.
Contained By:	Masters Abstracts International46-04.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR34985
ISBN:	9780494349854

Downloading data from textual deep Web using clustering.
Yuan, Xiaolei.

Downloading data from textual deep Web using clustering. - 69 p.

Source: Masters Abstracts International, Volume: 46-04, page: 2175.

Thesis (M.Sc.)--University of Windsor (Canada), 2007.

Keywords: deep web, hidden web data discovery, data mining, clustering, information retrieval.

ISBN: 9780494349854Subjects--Topical Terms:

626642
Computer Science.

Downloading data from textual deep Web using clustering.
LDR:01709nam 2200253 a 45 001 963696
005 20110831
008 110831s2007 ||||||||||||||||| ||eng d
020 $a 9780494349854
035 $a (UMI)AAIMR34985
035 $a AAIMR34985
040 $a UMI $c UMI
100 1 $a Yuan, Xiaolei. $3 1286759
245 1 0 $a Downloading data from textual deep Web using clustering.
300 $a 69 p.
500 $a Source: Masters Abstracts International, Volume: 46-04, page: 2175.
502 $a Thesis (M.Sc.)--University of Windsor (Canada), 2007.
520 $a Keywords: deep web, hidden web data discovery, data mining, clustering, information retrieval.
520 $a Deep web is the web that is dynamically generated from data sources such as databases or file systems. Crawling deep web is the process of collecting hidden data by issuing appropriate queries in order to download most of the data. Our main challenge is to select appropriate queries in order to obtain most of the data from a data source. A naive solution, which selects the queries that return most results, is problematic because (1) the results may not cover the data source, and more importantly, (2) the results suffer from high overlap, which makes the acquisition of new data items almost impossible after certain steps. The thesis experiments with four different algorithms to select the queries that minimize the overlap rate: (1) greedy algorithm based on set packing; (2)cluster-based algorithm to remove the queries that result in similar returns.
590 $a School code: 0115.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 $a University of Windsor (Canada). $3 1018526
773 0 $t Masters Abstracts International $g 46-04.
790 $a 0115
791 $a M.Sc.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR34985