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Student Number 80325045
Author Lee Kung([)
Author's Email Address No Public.
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Department Information and Electronic Engineering
Year 1992
Semester 2
Degree Master
Type of Document Master's Thesis
Language English
Title A Research on Team-Oriented Query Language and Its Processing on Parallel Computer
Date of Defense
Page Count 80
Keyword
  • Parallel genetic algorithms
  • Parallel heuristic algorithms
  • Team query
  • Team-oriented query language
  • Abstract In decision support application, different demands are often
    needed by a manager to find a team of persons or things
    satisfied with some constraints. We name this kind of applica-
    tions as team applications. Because it can not be supported in
    current existing relational query languages, thus leads to the
    repeated development of different team application programs. In
    order to enable such problems to be stated and solved directly
    by the relational database system leading to benefits of
    increased productivity, we presented a team query language TOQL
    and query processing algorithms to resolve this problem.
    Heuristic algorithms and parallel heuristic algorithms are
    first presented to process the team query. But the search space
    for possible teams is all subset of a relation, which is 2m for
    a m-element relation, to find the optimum solution with maximum
    or minimum value constraint may be of NP-hard. The heuristic
    algorithm cannot solve this problem. Therefore gene- tic
    algorithms (GAs) are used to speed the team query process- ing
    for this type of team query in order to give user response in
    an acceptable time period, for exeample: 7 seconds for data
    size 1000. Parallel genetic algorithms are also investigated to
    reduce the team query processing time. The test results
    compared to those of heuristic algorithms show that the
    improvement for convergence time is from exponential to linear
    and the converg- ence value difference is within 2.7% for the
    data size 1000 to 10000. Also the average speedup varies from
    0.90 x2 to 0.55 x8 at data size range 1000 to 10000. The test
    results justify that the parallel GA could be applied in the
    team query processing.
    Table of Content
    Reference
    Advisor
  • Chen Gwo Dong()
  • Files No Any Full Text File.
    Date of Submission

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