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Dynamic Cost Ant Colony Algorithm for Optimize Distributed Database Query

  • Sayed A. MohsinEmail author
  • Saad M. DarwishEmail author
  • Ahmed YounesEmail author
Conference paper
  • 89 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)

Abstract

Optimizing query in distributed database is considered as the most important part of a database system. The optimizer tries to find an optimal join order which reduces the query execution cost. Many factors may affect the execution cost of a query, including communication costs, resources, and access to large distributed data sets. When the number of relations and number of joins in a query increases, the complexity of the optimizer also increases. The success of query execution heavily influenced by the search method which is performed using the query optimizer. Processing of queries is considered as NP-hard problem and many researchers are focused on this problem in recent years. Researches are trying to build an appropriate algorithm to seek an optimal solution especially when the size of the database increases. In this paper, an ant colony algorithm as one of the hybrid strategy of evolutionary algorithms is utilized to find a solution for join query optimization problem in the distributed database systems. Unlike traditional ant colony-based query optimization techniques that based on static cost, the suggested model relies on dynamic cost which calculates the cost while the execution plan is built. Using this strategy, the algorithm aims to find an optimal join order which minimizes the total execution time. Experimental results show that the proposed model can handle different number of join entities. Also, the algorithm is affected by the number of ants used. Better results are obtained in case of large joined if the number of used ants increased.

Keywords

Distributed database system Join query Query optimization Ant Colony Optimization 

References

  1. 1.
    Ramakrishnan, R.: Databases Management Systems, 3rd edn. McGraw-Hill Inc., New York (2003)Google Scholar
  2. 2.
    Tiwari, M.P., Chande, S.V.: Query optimization strategies in distributed databases. Int. J. Adv. Eng. Sci. 3(3), 23–29 (2013)Google Scholar
  3. 3.
    Dökeroğlu, T., Coşar, A.: Dynamic programming with ant colony optimization metaheuristic for optimization of distributed database queries. In: Proceedings of 26th International Symposium on Computer and Information, pp. 107–113. Springer, London (2011)Google Scholar
  4. 4.
    Sharma, M., Singh, G., Singh, R.: A review of different cost-based distributed query optimizers. Progr. Artif. Intell. 8(1), 45–62 (2019)CrossRefGoogle Scholar
  5. 5.
    Hameurlain, A., Morvan, F.: Evolution of query optimization methods. Lect. Note Comput. Sci. 5740, 211–242 (2009)CrossRefGoogle Scholar
  6. 6.
    Chen, M., Yu, P.: Using join operations as reducers in distributed query processing. In: Proceedings of 2nd International Symposium on Databases in Parallel and Distributed System, July 1990Google Scholar
  7. 7.
    Pramanik, S., Vineyard, D.: Optimizing join queries in distributed database. IEEE Trans. Softw. Eng. 14, 1391–1426 (1988)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Rothnie, J.B., Bernstein, P.A., Fox, S.: Introduction to a system for distributed database. ACM Trans. Database Syst. 5(1), 1–17 (1980)CrossRefGoogle Scholar
  9. 9.
    Aljanaby, A., Abuelrub, E., Odeh, M.: A Survey of distributed query optimization. Int. Arab J. Inform. Technol. 2(1), 48–57 (2005)Google Scholar
  10. 10.
    Yannis, Y.C.K., Ioannidis, E.: Randomized algorithms for optimizing large join queries. ACM Sigmod Rec. 19(2), 312–321 (1990)CrossRefGoogle Scholar
  11. 11.
    Horng, J.T., Kao, C.Y., Jhiune, B.: A Genetic algorithm for database query optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, Orlando, FL, USA, pp. 432–444 (1994)Google Scholar
  12. 12.
    Sevinc, E., Cosar, A.: An evolutionary genetic algorithm for optimization of distributed database queries. Comput. J. 54(5), 717–725 (2010)CrossRefGoogle Scholar
  13. 13.
    Sukheja, D., Singh, U.: A Novel approach of query optimization for distributed database system. Int. J. Comput. Sci. 8(4), 307–312 (2011). No. 1Google Scholar
  14. 14.
    Dorigo, M., Birattari, M., Stützle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)CrossRefGoogle Scholar
  15. 15.
    Dorigo, M., Stuzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)CrossRefGoogle Scholar
  16. 16.
    Kossmann, D.: The state of art in distributed query optimization. ACM Comput. Surv. 32, 422–469 (2000)CrossRefGoogle Scholar
  17. 17.
    Kossmann, D., Stocker, K.: Iterative dynamic programming: a new class of query optimization algorithm. ACM Trans. Database Syst. 25, 43–82 (2000)CrossRefGoogle Scholar
  18. 18.
    Steinbrunn, M., Moerkotte, G., Kemper, A.: Heuristic and randomized optimization for the join-ordering problem. Int. J. Very Large Data Bases 6(3), 191–208 (1997)CrossRefGoogle Scholar
  19. 19.
    Zhou, Z.: Using heuristics and genetic algorithms for largescale database query optimization. J. Inform. Comput. Sci. 2(4), 261–280 (2007)Google Scholar
  20. 20.
    Rho, S., March, S.T.: Optimizing distributed join queries: a genetic algorithm approach. Ann. Oper. Res. 71, 199–228 (1997)CrossRefGoogle Scholar
  21. 21.
    Li, N., Liu, Y., Dong, Y., et al.: Application of ant colony optimization algorithm to multi-join query optimization. In: Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence. Springer, Wuhan (2008)Google Scholar
  22. 22.
    Golshanara, L., Rankoohi, S.M.T.R., Shah-Hosseini, H.: A multi-colony ant algorithm for optimizing join queries in distributed database systems. Knowl. Inform. Syst. 39(1), 175–206 (2014)CrossRefGoogle Scholar
  23. 23.
    Tiwari, P., Chande, S.: Optimal ant and join cardinality for distributed query optimization using ant colony optimization algorithm. In: Proceedings of the 2nd International Symposium on Emerging Trends in Expert Applications and Security, Singapore, February 2019Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Graduate Studies and ResearchAlexandria UniversityAlexandriaEgypt
  2. 2.Faculty of ScienceAlexandria UniversityAlexandriaEgypt

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