Dynamic materialized view selection based on two-phase optimization
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2013
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2556
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364 leaves
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b184488
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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National Institute of Development Administration. Library and Information Center
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Boontita Suchyukorn (2013). Dynamic materialized view selection based on two-phase optimization. Retrieved from: http://repository.nida.ac.th/handle/662723737/3026.
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Dynamic materialized view selection based on two-phase optimization
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Abstract
A Data Warehouses is a repository of information integrated from a
distributed data source. Information stored in a data warehouse is the form of the
materialized view. Materializing view is a technique to improve query response time
in a data warehouse. Deciding which of the appropriated views should be materialized
views is one of the significant problems in data warehouse design. In order to solve
this problem, constructing a search space close to optimal is a necessary task. It
provides effective results for the selection of views to be materialized. The Multiple
View Processing Plan (MVPP) is one of the several approaches to construct the
optimal search space for the view selection problem. However, some merged queries
in MVPP provide the query processing cost not close to optimal. Therefore, we
proposed the re-optimized MVPP algorithm to improve the query processing cost of
those queries by rewriting them using global common subexpression.
In the real situation, the requirements are frequently changed by the
stakeholder. Therefore, the existing materialized views and virtual views derived from
static materialized view selection should be considered whether they are still suitable
to support all requirements, the existing and new requirements. In this research, we
propose an approach for dynamic materialized view selection based on proposed reoptimized
MVPP algorithm. We propose the algorithm to determine the existing
materialized views and virtual views that are affected by changing the requirement
rather than all existing resource in the search space.
The experiment shows that our approach, the re-optimized MVPP, improves
the total query processing cost of MVPP. Also the summation of query processing
costs and materialized view maintenance costs are reduced after the set of views are
selected to be materialized by using the Two-Phase Optimization algorithm. For our
dynamic materialized view selection approach, the experiment shows that our
approach can specify the member of a set of views to be selected rather than all
existing views in the search space. It provides optimal total cost without recalculating
all requirements from scratch.
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Dissertations(Ph.D. (Computer Science))National Institute of Development Administration, 2013.