Research Article
Performance Tuning in Database Management System based on Analysis of Combination of Time and Cost Parameter through Neural Network Learning
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 96 - Issue 1 |
| Published: June 2014 |
| Authors: Bindu Sharma, Mahesh Singh |
10.5120/16761-6322
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Bindu Sharma, Mahesh Singh . Performance Tuning in Database Management System based on Analysis of Combination of Time and Cost Parameter through Neural Network Learning. International Journal of Computer Applications. 96, 1 (June 2014), 32-34. DOI=10.5120/16761-6322
@article{ 10.5120/16761-6322,
author = { Bindu Sharma,Mahesh Singh },
title = { Performance Tuning in Database Management System based on Analysis of Combination of Time and Cost Parameter through Neural Network Learning },
journal = { International Journal of Computer Applications },
year = { 2014 },
volume = { 96 },
number = { 1 },
pages = { 32-34 },
doi = { 10.5120/16761-6322 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2014
%A Bindu Sharma
%A Mahesh Singh
%T Performance Tuning in Database Management System based on Analysis of Combination of Time and Cost Parameter through Neural Network Learning%T
%J International Journal of Computer Applications
%V 96
%N 1
%P 32-34
%R 10.5120/16761-6322
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Performance tuning in database management system means enhancing the performance of database, i. e. minimizing the response time at a very optimum cost. Query optimization is one of the important aspects of performance tuning. Lots of research work has been done in this field but it is still ongoing process. To achieve high performance at a very low cost identification of KPIs (Key performance indicators) is necessary, so that by altering these parameters dynamically minimum response time with optimum value can be achieved. This paper proposes how to filter cost and time parameters, to prioritize these parameters to get minimum response time. The approach proposes in this paper will be implemented by using neural network learning rules.
References
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