Adaptive Tuning Algorithm Used in Multi-Join Query Optimization

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 288)

Abstract

The multi-join query optimization problem is hot and difficult in the data query optimization research field. Based on the study at cost estimation methods and the theory of multi-join queries, this paper gives an improved cost estimation model and a new search algorithm of query execution strategy space. The proposed optimization method uses adaptive genetic algorithm based on cloud theory in searching query strategy space. Simulation results demonstrate the effectiveness of the algorithm.

Keywords

Multi-join Query optimization Cost model Adaptive genetic algorithm Cloud theory 

Notes

Acknowledgments

This work was supported by the Co-Funding Project of Beijing Municipal Education Commission under Grant No. JD100060630.

References

  1. 1.
    Ahmed R, Lee A, Witkowski A, et al (2006) Cost-based query transformation in Oracle. Proceedings of the 32nd international conference on very large data bases, VLDB Endowment, 1026–1036Google Scholar
  2. 2.
    Swami A (1989) Optimization of large join queries: combining heuristics and combinatorial techniques, ACM SIGMOD Record, ACM, 18(2):367–376Google Scholar
  3. 3.
    Graefe G (1993) Query evaluation techniques for large databases. ACM Comput Surv (CSUR) 25(2):73–169CrossRefGoogle Scholar
  4. 4.
    Bennett KP, Ferris MC, Ioannidis YE (1991) A genetic algorithm for database query optimization. Computer Sciences Department, University of Wisconsin, Center for Parallel OptimizationGoogle Scholar
  5. 5.
    Guo L (2008) Research of query rewriting and multi-join query optimization based on GA of database. Northeastern University (in Chinese)Google Scholar
  6. 6.
    Guo C, Zhu L, Li X (2009) Multi-join query optimization method based on ant colony algorithm. Comput Eng, 35(10):173–175 (in Chinese) Google Scholar
  7. 7.
    Song L (2009) Research and application on multi-join query optimization of database based on genetic algorithm and simulated annealing. Changchun University of Technology (in Chinese)Google Scholar
  8. 8.
    Zhao X (2010) Design and implementation of query optimization algorithm based on genetic tabu search based on stack and chosen the superior. South China University of Technology (in Chinese)Google Scholar
  9. 9.
    Srinivas M, Patnaik LM (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4):656–667CrossRefGoogle Scholar
  10. 10.
    Liu Y (2008) Application and research on multi-join query optimization of database based on GA. Daqing Petroleum Institute (in Chinese)Google Scholar
  11. 11.
    Dai C, Zhu Y, Chen W (2007) Adapative genetic algorithm based on cloud theory. Control Theory Appl 24(4):646–650 (in Chinese)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Network Technology, School of Computer Science and EngineeringBeihang University (BUAA)BeijingChina

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