• DocumentCode
    3244559
  • Title

    Data mining using classification techniques in query processing strategies

  • Author

    Teh, Ying Wah ; Abu Bakar Zaitun ; Lee, Sai Peck

  • Author_Institution
    Fac. of Comput. Sci. & Inf. Technol., Malaya Univ., Kuala Lumpur, Malaysia
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    200
  • Lastpage
    202
  • Abstract
    Given a query, there are many different query processing strategies to fulfil user requirements. The selection of tuples are from 0% to 100%. The found set of relations will determine the different query processing strategies being implemented. To implement effective query processing strategies relies on the cost model of query processing. Traditionally, query processing does not handle personalization of user requirements. In the e-commerce environment, in order to achieve a fast response time of a query it requires personalization in the relation. We introduce the concept of personalization at the query processing level. We discuss a cost model for each of the query processing strategies and use a data mining technique such as classification in selecting the most effective query processing strategy for personalization. We introduce the current query processing strategies. We conclude with a data mining technique as an alternative in selecting a query processing strategy
  • Keywords
    classification; data mining; electronic commerce; query processing; relational databases; software performance evaluation; classification; cost model; data mining; e-commerce; personalization; query processing; relational database; response time; tuples; user requirements; Algorithm design and analysis; Classification algorithms; Computer science; Costs; Data mining; Delay; Displays; Information technology; Partitioning algorithms; Query processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Systems and Applications, ACS/IEEE International Conference on. 2001
  • Conference_Location
    Beirut
  • Print_ISBN
    0-7695-1165-1
  • Type

    conf

  • DOI
    10.1109/AICCSA.2001.933977
  • Filename
    933977