Optimization of constrained queries with a hybrid genetic algorithm

  • Augustine Chidi Ikeji
  • Farshad Fotouhi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1460)


Optimization of constrained queries is a growing area of DBMS. Databases are getting larger and users are demanding fast and efficient algorithms for processing queries. Users want the ability to limit the query output set, processing time, or number of records analyzed. Processors have self-imposed constraints which force them to process queries with as little resource as possible. Under these constraints, processors may not be able to produce an entire result, and have to render an optimal partial result. We propose an algorithm that combines statistical sampling and the genetic algorithms idea of survival of the fittest to identify and concentrate in areas of the relation “better fit” for the query result. Our algorithm is also compared to the conventional ones.


Query Optimization Genetic Algorithms Statistical Sampling Constrained Queries 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    A. C. Ikeji, and F. Fotouhi, “Computation of Partial Query results With An Adaptive Stratified Sampling Technique”, Proceedings of the Fourth International Conference on Information and Knowledge Management, pp. 145–149, 1995Google Scholar
  2. 2.
    N. C. Rowe, “Antisampling for Estimation: An Over-view”, IEEE transactions on Software Engineering, pp. 1081–1091, October 1985Google Scholar
  3. 3.
    P. J. Haas, and A. N. Swami, “Sequential Sampling Procedures For Query Size Estimation”, ACM SIGMOD, pp. 341–350, June 1992Google Scholar
  4. 4.
    Y. C. Tay, “On the Optimality of Strategies for Multiple Joins”, Journal of the ACM, 40, 5, pp. 1067–1086, November 1993MATHMathSciNetCrossRefGoogle Scholar
  5. 5.
    E. Ramez, and S. B. Navathe, “Fundamentals of Database Systems”, The Benjamin/Cummings Publishing Company, Inc., Redwood City, California, 1989Google Scholar
  6. 6.
    R. Lipton, J. Naughton, and D. Schneider, “Practical Selectivity Estimation through Adaptive Sampling”, Proceedings of the ACM SIGMOD, pp. 1–11, 1990Google Scholar
  7. 7.
    G. T. Henry, “Practical Sampling”, SAGE Publications, Inc., Newbury Park, California, 1990Google Scholar
  8. 8.
    F. Olken, and D. Rotem, “Random Sampling from B+ trees”, Proceedings of the 5th International Conference on VLDB, pp. 269–278, August 1989Google Scholar
  9. 9.
    Y. Ling, and W. Sun, “A supplement to Sampling-Based Methods for Query Size Estimation in Database System”, Proceedings of the ACM SIGMOD, pp. 12–15, 1992Google Scholar
  10. 10.
    L. D. Whitley, “Foundations of Genetic Algorithms 2”, Morgan Kaufman Publishers, California, 1993Google Scholar
  11. 11.
    P.G. Selfridge, D. Srivastava, and L.O. Wilson, “Interactive Data Exploration And Analysis”, Proceeding of SIGMOD-96, ACM Press, New York, pp. 24–34, Montreal, June 1996.Google Scholar
  12. 12.
    M. Schwartz, “Theory of Statistics”, Springer-Verlag, New York, 1995.Google Scholar
  13. 13.
    L. Davis, “Handbook of Genetic Algorithms”, Van Nostrand Reinhold, New York, 1991Google Scholar
  14. 14.
    D. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning”, Addison-Wesley Publishing Company, Inc., Massachusetts, 1989Google Scholar
  15. 15.
    A. J. Wilburn, “Practical Statistical Sampling for Auditors”, Marcel Dekker, Inc., New York, 1984Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Augustine Chidi Ikeji
    • 1
  • Farshad Fotouhi
    • 2
  1. 1.Computer Science DepartmentEastern Michigan UniversityYpsilanti
  2. 2.Computer Science DepartmentWayne State UniversityDetroit

Personalised recommendations