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Improving database performance with a mixed fragmentation design.

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Title: Improving database performance with a mixed fragmentation design.
Authors: Gorla, Narasimhaiah1 ngorla@aus.edu, Ng, Vincent2, Law, Dik2
Source: Journal of Intelligent Information Systems. Dec2012, Vol. 39 Issue 3, p559-576. 18p.
Subjects: Databases, Storage fragmentation (Computer science), Document clustering, Logic partitioning (Virtual machine systems), Computer network cost control
Abstract: The performance of database operations can be enhanced with an efficient storage structure design using attribute partitioning and/or tuple clustering. Previous research deals mostly with attribute partitioning. We address here the combined problem of attribute partitioning and tuple clustering. We propose a novel approach for this mixed fragmentation problem by applying a genetic algorithm iteratively to attribute partitioning and tuple clustering sub-problems. We compared our results to attribute-only partitioning and random search solution, resulting in a database access cost reduction of upto 70% and 67% respectively. We analyzed the effect of varying genetic parameters on the optimal solution through experimentation. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Intelligent Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Gorla%2C+Narasimhaiah%22">Gorla, Narasimhaiah</searchLink><relatesTo>1</relatesTo><i> ngorla@aus.edu</i><br /><searchLink fieldCode="AR" term="%22Ng%2C+Vincent%22">Ng, Vincent</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Law%2C+Dik%22">Law, Dik</searchLink><relatesTo>2</relatesTo>
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  Data: <searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Storage+fragmentation+%28Computer+science%29%22">Storage fragmentation (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Document+clustering%22">Document clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Logic+partitioning+%28Virtual+machine+systems%29%22">Logic partitioning (Virtual machine systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+cost+control%22">Computer network cost control</searchLink>
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  Data: The performance of database operations can be enhanced with an efficient storage structure design using attribute partitioning and/or tuple clustering. Previous research deals mostly with attribute partitioning. We address here the combined problem of attribute partitioning and tuple clustering. We propose a novel approach for this mixed fragmentation problem by applying a genetic algorithm iteratively to attribute partitioning and tuple clustering sub-problems. We compared our results to attribute-only partitioning and random search solution, resulting in a database access cost reduction of upto 70% and 67% respectively. We analyzed the effect of varying genetic parameters on the optimal solution through experimentation. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Intelligent Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Computer network cost control
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