CFP last date
22 July 2024
Reseach Article

Query Optimization using Multiple Techniques

by Ajay Wagh, Varsha Nemade
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 3
Year of Publication: 2017
Authors: Ajay Wagh, Varsha Nemade

Ajay Wagh, Varsha Nemade . Query Optimization using Multiple Techniques. International Journal of Computer Applications. 163, 3 ( Apr 2017), 30-32. DOI=10.5120/ijca2017913490

@article{ 10.5120/ijca2017913490,
author = { Ajay Wagh, Varsha Nemade },
title = { Query Optimization using Multiple Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 163 },
number = { 3 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 30-32 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017913490 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:09:11.738401+05:30
%A Ajay Wagh
%A Varsha Nemade
%T Query Optimization using Multiple Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 163
%N 3
%P 30-32
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

Query optimization is the overall process of choosing the most efficient means of executing a SQL statement. The optimizer attempts to generate the best execution plan for a SQL statement. The best execution plan is defined as the plan with the lowest cost among all considered candidate plans. SQL is a nonprocedural language, so the optimizer is free to merge, reorganize, and process in any order. The cost is a number that represents the estimated resource usage for an execution plan. The cost computation accounts for factors of query execution such as I/O, CPU, and communication. To implement query optimization methods such as Heuristic Greedy based optimization, Iterative Improvement based cost optimization and Ant Colony optimization algorithms. Show Comparison of cost, execution time and response time between Heuristic Greedy based optimization; Ant Colony Optimization and Iterative Improvement based cost optimization algorithms.

  1. Duy-Hung Phan et al.“A Novel, Low-latency Algorithm
for Multiple Group-By Query Optimization” ICDE 2016 Conference ,IEEE , 978-1-5090-2020-1/16 2016 IEEE.
  2. Vishal P. Patel, Hardik R. Kadiya “Optimization of Large Join Query using Heuristic Greedy Algorithm ” IJCAT - International Journal of Computing and Technology Volume 1, Issue 1, February 2014
  3. Myungcheol Lee et al. “A JIT Compilation-based Unified SQL Query Optimization System” 978-1-5090-3765-0/16/ ©2016 IEEE
  4. Saurabh gupta ,Gopal Singh Tandel ,Umashankar Pandey , “A Survey on Query Processing and Optimization in Relational Database Management System ”, International Journal of Latest Trends in Engineering and Technology (IJLTET) ,Vol. 5 Issue 1 January 2015 , ISSN: 2278-621X
  5. Dr. G. R. Bamnote Professor & Head Dept. of CSE, PRMITR, Badnera, India ,Prof. S. S. Agrawal ,Asst. Prof Dept. of CSE, COE & T, Akola, India ,” Introduction to Query Processing and Optimization ”, International Journal of Advanced Research in Computer Science and Software Engineering , Volume 3, Issue 7, July 2013 ISSN: 2277 128X
  6. A. K. Giri and R. Kumar, “Distributed query processing plan generation using iterative mprovement and simulated annealing,” 2013 IEEE 3rd International Advance Computing Conference, pp. 757-762, Feb. 2013.
  7. T. Kumar, V. Singh and A. K. Verma, “Distributed query processing plans generation using genetic algorithm,” International Journal of Computer Theory and Engineering, pp. 38-45, 2011.
  8. Melanie Mitchell, “An introduction to Genetic Algorithms”, Prentice Hall of India, 2004
  9. Hsiung Sam, Matthews James, “An introduction to Genetic Algorithms”, 2000
Index Terms

Computer Science
Information Sciences


Query Optimization Heuristic-based optimizers Ant-Colony