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Reseach Article

Reducing Execution Time of Distributed SELECT Query in Heterogeneous Distributed Database using Genetic Algorithm

by Nikhil S. Gajjam, S. S. Apte
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 7
Year of Publication: 2014
Authors: Nikhil S. Gajjam, S. S. Apte
10.5120/17539-8119

Nikhil S. Gajjam, S. S. Apte . Reducing Execution Time of Distributed SELECT Query in Heterogeneous Distributed Database using Genetic Algorithm. International Journal of Computer Applications. 100, 7 ( August 2014), 34-38. DOI=10.5120/17539-8119

@article{ 10.5120/17539-8119,
author = { Nikhil S. Gajjam, S. S. Apte },
title = { Reducing Execution Time of Distributed SELECT Query in Heterogeneous Distributed Database using Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 7 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 34-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number7/17539-8119/ },
doi = { 10.5120/17539-8119 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:29:21.537937+05:30
%A Nikhil S. Gajjam
%A S. S. Apte
%T Reducing Execution Time of Distributed SELECT Query in Heterogeneous Distributed Database using Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 7
%P 34-38
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Centralized unit that coordinates different types of schema running on multiple sites is getting importance now-a-days. Heterogeneous Distributed Database System (HDDS) is the collection of multiple different databases management systems running on multiple systems that are linked together. Query processing is complicated in such cases. In this work we concentrate on utilizing Genetic Algorithm for finding optimized query execution plan for distributed SELECT queries. Selecting the right set of plans for queries using Genetic Algorithm which minimizes the total execution time is the major goal of this work. Replication of schema is used in this work which gives multiple solutions for retrieval of the data. We used Chromosome for specifying plan for query. Chromosome structures consist of combination of data site and join order. Aglet, Mobile agent, is used to connect all the database servers with centralized server. By implementing this, we get the optimized plan for the select query.

References
  1. Bennet K, Ferris MC, Ioannidis YE (1991) A genetic algorithm for database query optimization. In: Proc 4th Int Conf Genetic Algorithms, SanDiego,Calif,pp400–407 2011
  2. B. Lange, D. T. Chang, ÏBM Aglets Workbench - Programming Mobile Agents in Java", IBM Corporation White Paper, September 1996. .
  3. D. B. Lange, Mobile Objects and mobile agents: The future of distributed computing? In Proceedings of The European Conference on Object-Oriented Programming, 1998.
  4. D. B. Lange and M. Oshima, "Mobile agents with Java: The Aglet API," World Wide Web Journal,1998.
  5. Ender Sevinc and Ahmet Co¸sar(2011). An Evolutionary Genetic Algorithm for Optimization of Distributed Database Queries. The Computer Journal, Vol. 54 No. 5
  6. E. -P. Lim and J. Srivastava(1993), 'Query optimization/processing in federated database systems', in Conference of Information and Knowledge Management.
  7. Joachim Baumann, Fritz Hohl, Kurt Rothermel, and Markus Straßer. Mole— concepts of a mobile agent system. World Wide Web Journal, 1(3):123–137,1998.
  8. Michael L. Rupley, Jr. (2008): Introduction to Query Processing and Optimization. http://www. cs. iusb. edu/technical_repots/TR- 20080105-1. pdf
  9. Murat Ali Bayir, Ismail H. Toroslu, and Ahmet Cosar(2007). Genetic Algorithm for the Multiple-Query Optimization Problem. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 37, NO. 1
  10. M. Stillger and M. Spiliopoulou, "Genetic programming in database query optimization," in Proc First Annu. Conf. Genetic Programming,Stanford, CA, July 1996
  11. N. B. Herodotos Herodotou and S. Babu. Query optimization techniques for partitioned tables. In SIGMOD Conference,pages49–60,
  12. Reza Ghaemi, Amin Milani Fard, Hamid Tabatabaee, and Mahdi Sadeghizadeh September 2008, Evolutionary Query Optimization for Heterogeneous Distributed Database Systems. World Academy of Science
  13. Smith, J. M. , P. A. Bernstein, U. Dayal, N. Goodman, T. Landers, K. W. T. Lin, E. Wong. MULTIBASE -- Integrating heterogeneous distributed database systems. Proceedings of 1981 National Computer Conference, AFIPS Press, 487-499
  14. T. V. Vijay Kumar, Vikram Singh, "Distributed Query Processing Plans Generation Using GA", IJCTE, Vol 3. No. 1, Feb 2011
  15. Wiesman, M. , et al. Database Replication Techniques: A Three Paramater Classification. in 19th IEEE Symposium on Reliable Distributed Systems. 2000. Nuernberg, Germany
  16. Y. . E. Ioannidis and Y. C. Kang, "Randomized algorithms for optimizing large join queries, ACM 1990
Index Terms

Computer Science
Information Sciences

Keywords

Heterogeneous Distributed Database Genetic Algorithm Aglet.