• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • An improved selection-based parallel range-join algorithm in hypercubes

    Author(s)
    Shen, Hong
    Griffith University Author(s)
    Shen, Hong
    Year published
    1994
    Metadata
    Show full item record
    Abstract
    The range-join of two sets R and S is the set that contains all tuples (r, s) satisfying e1⩽|r-s|⩽e2, r?R and s?S. For computing the range-join of R and S in a hypercube of p processors, this paper presents an improved selection-based parallel algorithm which reduces the local memory from O(n) repaired in the previous algorithm to O(m+n/p), where |R|=m, |S|=n and p⩽max{m,n}. The new algorithm also reduces the best-case time complexity from O(m/p log2 p+n/p log m) of the previous result to O(m+n/p log2p) when m⩾plog, while maintaining the cost optimality in the worst case. Unlike the previous algorithm, our ...
    View more >
    The range-join of two sets R and S is the set that contains all tuples (r, s) satisfying e1⩽|r-s|⩽e2, r?R and s?S. For computing the range-join of R and S in a hypercube of p processors, this paper presents an improved selection-based parallel algorithm which reduces the local memory from O(n) repaired in the previous algorithm to O(m+n/p), where |R|=m, |S|=n and p⩽max{m,n}. The new algorithm also reduces the best-case time complexity from O(m/p log2 p+n/p log m) of the previous result to O(m+n/p log2p) when m⩾plog, while maintaining the cost optimality in the worst case. Unlike the previous algorithm, our algorithm works by selecting the median of RUS to evenly partition the whole data set for divide-and-conquer join in the next phase. We present an upper bound of time complexity of the algorithm in the general case and show that the best-case time complexity of the algorithm is better than permutation-based range-join when n⩾plogp+1
    View less >
    Publisher URI
    http://ieeexplore.ieee.org/servlet/opac?punumber=3105
    DOI
    https://doi.org/10.1109/EURMIC.1994.390405
    Subject
    Environmental Sciences
    Publication URI
    http://hdl.handle.net/10072/27876
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander