• DocumentCode
    3311891
  • Title

    Locality sensitive hashing based searching scheme for a massive database

  • Author

    Shen, Haiying ; Li, Ting ; Li, Ze ; Ching, Felix

  • Author_Institution
    Univ. of Arkansas, Fayetteville
  • fYear
    2008
  • fDate
    3-6 April 2008
  • Firstpage
    123
  • Lastpage
    128
  • Abstract
    The rapid growth of information nowadays makes efficient information searching increasingly important for a massive database with tremendous volume of information. Traditional methods either rely on linear searching or depend on a tree structure. These methods search information in the entire database and compare a query with the records in the database during the searching process, which lead to inefficiency. This paper presents a locality sensitive hashing based searching scheme (LSS) to achieve highly efficient information searching in a massive database. LSS classifies information based on their similarities to facilitate fast information location. Based on the study and analysis of LSS, an improved scheme is further proposed to enhance the searching efficiency. Simulation results demonstrate the efficiency and effectiveness of the LSS schemes in searching information. They yield significant improvements over the efficiency of traditional methods. In addition, they guarantee successful location of the queried records.
  • Keywords
    file organisation; information retrieval; very large databases; information classification; information searching; locality sensitive hashing; massive database; Computer science; Data engineering; Degradation; Distance measurement; Hamming distance; Image databases; Nearest neighbor searches; Spatial databases; Terrorism; Tree data structures;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Southeastcon, 2008. IEEE
  • Conference_Location
    Huntsville, AL
  • Print_ISBN
    978-1-4244-1883-1
  • Electronic_ISBN
    978-1-4244-1884-8
  • Type

    conf

  • DOI
    10.1109/SECON.2008.4494271
  • Filename
    4494271