• DocumentCode
    2015883
  • Title

    BloomCast: Efficient Full-Text Retrieval over Unstructured P2Ps with Guaranteed Recall

  • Author

    Chen, Hanhua ; Jin, Hai ; Luo, Xucheng ; Liu, Yunhao ; Ni, Lionel M.

  • Author_Institution
    Sch. of Comput. Sci. & Tech., Huazhong Univ. of Sci. & Tech., Wuhan
  • fYear
    2009
  • fDate
    18-21 May 2009
  • Firstpage
    52
  • Lastpage
    59
  • Abstract
    Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P search protocols to effectively locate items with guaranteed recall rate. Second, existing schemes to improve search successful rate often rely on replicating a large number of item replicas across the wide area network, incurring a large amount of communication and storage cost. In this paper we propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. BloomCast is effective because it guarantees perfect recall rate with high probability. It is efficient because the overall communication cost of full-text search is reduced below a formal bound. Furthermore, by casting Bloom Filters instead of the raw documents across the network, BloomCast significantly reduces the communication cost and storage cost for replication. We demonstrate the power of BloomCast design through both mathematical proof and comprehensive simulations. Results show that BloomCast outperforms existing schemes in terms of both recall rate and communication cost.
  • Keywords
    information retrieval; peer-to-peer computing; BloomCast; full-text retrieval; guaranteed recall; peer-to-peer networks; Casting; Costs; Filters; Peer to peer computing; Protocols; Wide area networks; DHT; Peer-to-Peer; Search; Unstructured Overlay;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cluster Computing and the Grid, 2009. CCGRID '09. 9th IEEE/ACM International Symposium on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-3935-5
  • Electronic_ISBN
    978-0-7695-3622-4
  • Type

    conf

  • DOI
    10.1109/CCGRID.2009.50
  • Filename
    5071854