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A Database Tuning Framework for Improving Stored Procedure Performance.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Database Tuning Framework for Improving Stored Procedure Performance./
Author:	Suver, Nathan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	86 p.
Notes:	Source: Masters Abstracts International, Volume: 82-04.
Contained By:	Masters Abstracts International82-04.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28091910
ISBN:	9798678142139

A Database Tuning Framework for Improving Stored Procedure Performance.
Suver, Nathan.

A Database Tuning Framework for Improving Stored Procedure Performance. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 86 p.

Source: Masters Abstracts International, Volume: 82-04.

Thesis (M.S.)--Southern Connecticut State University, 2020.

This item must not be sold to any third party vendors.

Stored procedures are powerful and flexible language constructs that are implemented in many modern relational database management systems. They typically implement a mix of both procedural and declarative syntax and run directly inside the database engine, making them powerful tools for manipulating data. As database systems rapidly grow in size, however, they can exhibit noticeable scalability limitations that can be difficult to correct with traditional database best practices, such as proper indexing. This work introduces a framework that can be used to identify stored procedures which significantly impact system resources, based on an objective function. Database metadata is then leveraged to guide developers towards applying some well known refactor approaches to improve both procedure and database system performance. In addition, it is shown that by focusing the framework on a particular performance indicator, a better margin of improvement for that indicator can be achieved compared to a baseline. The framework is validated against an existing commercial healthcare software system.

ISBN: 9798678142139Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Database tuning framework

A Database Tuning Framework for Improving Stored Procedure Performance.
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100 1 $a Suver, Nathan. $3 3683375
245 1 0 $a A Database Tuning Framework for Improving Stored Procedure Performance.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 86 p.
500 $a Source: Masters Abstracts International, Volume: 82-04.
500 $a Advisor: Abd El-Raouf, Amal;Wu, Hao.
502 $a Thesis (M.S.)--Southern Connecticut State University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Stored procedures are powerful and flexible language constructs that are implemented in many modern relational database management systems. They typically implement a mix of both procedural and declarative syntax and run directly inside the database engine, making them powerful tools for manipulating data. As database systems rapidly grow in size, however, they can exhibit noticeable scalability limitations that can be difficult to correct with traditional database best practices, such as proper indexing. This work introduces a framework that can be used to identify stored procedures which significantly impact system resources, based on an objective function. Database metadata is then leveraged to guide developers towards applying some well known refactor approaches to improve both procedure and database system performance. In addition, it is shown that by focusing the framework on a particular performance indicator, a better margin of improvement for that indicator can be achieved compared to a baseline. The framework is validated against an existing commercial healthcare software system.
590 $a School code: 0928.
650 4 $a Computer science. $3 523869
650 4 $a Systems science. $3 3168411
650 4 $a Information technology. $3 532993
653 $a Database tuning framework
653 $a Stored procedure
653 $a Database management system
690 $a 0984
690 $a 0489
690 $a 0790
710 2 $a Southern Connecticut State University. $b Computer Science. $3 2103594
773 0 $t Masters Abstracts International $g 82-04.
790 $a 0928
791 $a M.S.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28091910