A database optimization model with quantitative benchmark

Query optimization and indexing have an immense impact on database optimization. This has been considered in many different perspectives which provide several different solutions in each case. The purpose of this paper is to primarily provide a comprehensive review and discussion of the core problem...

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Main Authors: Jayakody, J., Murray, Iain, Herrmann, J., Lokuliyana, S., Kandawala, D., Nanayakkara, S.
Format: Conference Paper
Published: 2016
Online Access:http://hdl.handle.net/20.500.11937/17197
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author Jayakody, J.
Murray, Iain
Herrmann, J.
Lokuliyana, S.
Kandawala, D.
Nanayakkara, S.
author_facet Jayakody, J.
Murray, Iain
Herrmann, J.
Lokuliyana, S.
Kandawala, D.
Nanayakkara, S.
author_sort Jayakody, J.
building Curtin Institutional Repository
collection Online Access
description Query optimization and indexing have an immense impact on database optimization. This has been considered in many different perspectives which provide several different solutions in each case. The purpose of this paper is to primarily provide a comprehensive review and discussion of the core problems through the research on query optimization technology and indexing, based on a number of optimization techniques commonly used in the general approach of a query. A new database optimization model is designed and experiments show that this model can significantly reduce the amount of query execution time to improve the optimization efficiency. Regardless of research on the database optimization model, significant work has been done to comparatively evaluate three scenarios; non-optimized query, optimized query without following any optimization standard, and optimized query via proposed optimization model using the same experimental methodology. Further, this paper describes a benchmark that was developed specifically for the purpose of measuring the quantitative evaluations of the proposed database optimization model.
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format Conference Paper
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T07:20:18Z
publishDate 2016
recordtype eprints
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spelling curtin-20.500.11937-171972017-09-13T13:36:44Z A database optimization model with quantitative benchmark Jayakody, J. Murray, Iain Herrmann, J. Lokuliyana, S. Kandawala, D. Nanayakkara, S. Query optimization and indexing have an immense impact on database optimization. This has been considered in many different perspectives which provide several different solutions in each case. The purpose of this paper is to primarily provide a comprehensive review and discussion of the core problems through the research on query optimization technology and indexing, based on a number of optimization techniques commonly used in the general approach of a query. A new database optimization model is designed and experiments show that this model can significantly reduce the amount of query execution time to improve the optimization efficiency. Regardless of research on the database optimization model, significant work has been done to comparatively evaluate three scenarios; non-optimized query, optimized query without following any optimization standard, and optimized query via proposed optimization model using the same experimental methodology. Further, this paper describes a benchmark that was developed specifically for the purpose of measuring the quantitative evaluations of the proposed database optimization model. 2016 Conference Paper http://hdl.handle.net/20.500.11937/17197 10.1109/ICCTICT.2016.7514594 fulltext
spellingShingle Jayakody, J.
Murray, Iain
Herrmann, J.
Lokuliyana, S.
Kandawala, D.
Nanayakkara, S.
A database optimization model with quantitative benchmark
title A database optimization model with quantitative benchmark
title_full A database optimization model with quantitative benchmark
title_fullStr A database optimization model with quantitative benchmark
title_full_unstemmed A database optimization model with quantitative benchmark
title_short A database optimization model with quantitative benchmark
title_sort database optimization model with quantitative benchmark
url http://hdl.handle.net/20.500.11937/17197