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Reseach Article

A Survey of Various Query Optimization Techniques

by Rini John, Nikita Palaskar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 173 - Number 5
Year of Publication: 2017
Authors: Rini John, Nikita Palaskar
10.5120/ijca2017915286

Rini John, Nikita Palaskar . A Survey of Various Query Optimization Techniques. International Journal of Computer Applications. 173, 5 ( Sep 2017), 36-38. DOI=10.5120/ijca2017915286

@article{ 10.5120/ijca2017915286,
author = { Rini John, Nikita Palaskar },
title = { A Survey of Various Query Optimization Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 173 },
number = { 5 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 36-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume173/number5/28335-2017915286/ },
doi = { 10.5120/ijca2017915286 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:20:29.849131+05:30
%A Rini John
%A Nikita Palaskar
%T A Survey of Various Query Optimization Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 173
%N 5
%P 36-38
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data storage and retrieving the data on a specific time frame is critical for any application today. So an efficiently designed query lets the user get the results in the desired time and creates the credibility for the corresponding application. Here in this paper various techniques which are currently used or proposed in recent years and to get a better perspective in this field of query optimization are explored. Automatic external SQL-query optimization method is where the principle of building queries regardless of applied database management system and its settings is explored. It would be interesting to explore the work of authors where they have proposed the energy-efficient query processing and optimization based on a database accelerator.

References
  1. Nicoleta Angelescu, Henri George Coanda, Ion Caciula*, Catalin Dragoi and Felix Albu., “SQL Query Optimization in Content Based Image Retrieval Systems”, IEEE 2016 International Conference on Communications (COMM)
  2. Varun Garg, “Optimization of Multiple Queries for Big Data with Apache Hadoop/Hive.”, IEEE 2015 International Conference on Computational Intelligence and Communication Networks (CICN).
  3. Sebastian Haas; Oliver Arnold; Stefan Scholze; Sebastian Höppner;  Georg Ellguth; Andreas Dixius; Annett Ungethüm; Eric Mier; Benedikt Nöthen; Emil Matúš; Stefan Schiefer; Love Cederstroem; Fabian Pilz; Christian Mayr; René Schüffny; Wolfgang Lehner; Gerhard P. Fettweis, “A Database Accelerator for Energy-Efficient Query Processing and Optimization”, 2016 IEEE Nordic Circuits and Systems Conference (NORCAS) in Proc. Int. Conf.
  4. Myungcheol Lee; Miyoung Lee; ChangSoo Kim, “A JIT Compilation-based Unified SQL Query Optimization System”, 2016 IEEE 6th International Conference on IT Convergence and Security (ICITCS).
  5. M. Zagirnyak; P. Kostenko, “External optimization of SQL-query under conditions of databases structural uncertainty”, 2016 IEEE 17th International Conference Computational Problems of Electrical Engineering (CPEE)
Index Terms

Computer Science
Information Sciences

Keywords

Query Optimization Tensilica RISC processor Hadoop OLTP OLAP Multi Query Optimization (MQO).