DocumentCode :
2301911
Title :
Selection of Deep Web Database Based on Retrieval Performance
Author :
Li, Weijing ; Yuan, Fang ; Zhang, Ming
Author_Institution :
Key Lab. in Machine Learning & Comput. Intell., Hebei Univ., Baoding, China
Volume :
3
fYear :
2010
fDate :
6-7 March 2010
Firstpage :
72
Lastpage :
75
Abstract :
A mass of high-quality information included in Deep Web can be accessed, which is still growing rapidly with the rapid development of the World Wide Web. Therefore it becomes more and more important to find the Web databases which are most relevant to the user queries. In this paper we propose a selection method of Web database based on retrieval performance. This method can fix the topic based on website characteristics and then classify the websites. Finally it decides which Web database can be chosen based on the retrieval performance. This method can not only accurately select the Web databases which satisfy the user queries but also improve the speed of the database query and the quality of retrieval.
Keywords :
Web sites; information retrieval; query languages; query processing; World Wide Web; database query; deep Web database; retrieval performance; user queries; website characteristics; Books; Computer science; Computer science education; Content based retrieval; Costs; Databases; Educational technology; Engines; Information retrieval; Machine learning; Web database selection; component; retrieval performance; topic;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Education Technology and Computer Science (ETCS), 2010 Second International Workshop on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6388-6
Electronic_ISBN :
978-1-4244-6389-3
Type :
conf
DOI :
10.1109/ETCS.2010.613
Filename :
5459955
Link To Document :
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