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This study optimizes distributed query plan generation (DQPG) by treating it as a biobjective problem. Using NSGA-II, it effectively minimizes both local processing cost and communication cost for distributed databases.

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Area of Science:

  • Computer Science
  • Database Systems
  • Artificial Intelligence

Background:

  • Distributed query processing is crucial for database performance.
  • Generating optimal query plans is complex due to exponential possibilities.
  • Previous methods used single-objective genetic algorithms.

Purpose of the Study:

  • To formulate and solve the distributed query plan generation (DQPG) problem as a biobjective optimization.
  • To simultaneously minimize local processing cost (LPC) and communication cost (CC).

Main Methods:

  • Formulated DQPG as a biobjective optimization problem.
  • Employed the multiobjective genetic algorithm NSGA-II for simultaneous optimization.
  • Conducted experimental comparisons against single-objective genetic algorithms.

Main Results:

  • The NSGA-II based approach demonstrated superior performance.
  • The algorithm converged more rapidly towards optimal solutions.
  • Biobjective optimization effectively balanced LPC and CC.

Conclusions:

  • Multiobjective optimization using NSGA-II is effective for DQPG.
  • This approach offers improved performance over single-objective methods.
  • Efficient query plan generation is vital for distributed database systems.