• DocumentCode
    3123836
  • Title

    Adaptive Multi-join Query Processing in PDBMS

  • Author

    Wu, Sai ; Vu, Quang Hieu ; Li, Jianzhong ; Kian-Lee Tan

  • Author_Institution
    Sch. of Comput., Nat. Univ. of Singapore, Singapore
  • fYear
    2009
  • fDate
    March 29 2009-April 2 2009
  • Firstpage
    1239
  • Lastpage
    1242
  • Abstract
    Traditionally, distributed databases assume that the (small) set of nodes participating in a query is known apriori, the data is well placed, and the statistics are readily available. However, these assumptions are no longer valid in a peer-based database management system (PDBMS). As such, it is a challenge to process and optimize queries in a PDBMS. In this paper, we present our distributed solution to this problem for multi-way join queries. Our approach first processes a multi-way join query based on an initial query evaluation plan (generated using statistical data that may be obsolete or inaccurate); as the query is being processed, statistics obtained on-the-fly are used to (continuously) refine the current plan dynamically into a more effective one. We have conducted an extensive performance study which shows that our adaptive query processing strategy can reduce the network traffic significantly.
  • Keywords
    database management systems; peer-to-peer computing; query processing; statistical analysis; PDBMS; adaptive multijoin query processing; peer-based database management system; statistical data; Buildings; Data engineering; Database systems; Distributed computing; Distributed databases; Indexing; Peer to peer computing; Query processing; Runtime; Statistical distributions; Adaptive Multi-Join; P2P;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Engineering, 2009. ICDE '09. IEEE 25th International Conference on
  • Conference_Location
    Shanghai
  • ISSN
    1084-4627
  • Print_ISBN
    978-1-4244-3422-0
  • Electronic_ISBN
    1084-4627
  • Type

    conf

  • DOI
    10.1109/ICDE.2009.210
  • Filename
    4812510