• 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