| Author: | Lau, Chung-fun |
| Title: | Determination of 'near optimal' granularity for efficient data warehouse operations |
| Degree: | M.Sc. |
| Year: | 2000 |
| Subject: | Data warehousing Hong Kong Polytechnic University -- Dissertations |
| Department: | Multi-disciplinary Studies Department of Computing |
| Pages: | iii, 101 leaves : ill. ; 30 cm |
| Language: | English |
| Abstract: | The objective of this project is to find the 'near optimal' materialized views in the data warehouse environment. The resulting views can answer all the queries of interest while minimizing the total query evaluation cost under a given space constraint. This project addresses how to select a suitable granularity level of aggregates (both in horizontal and vertical granularity level) from a set of materialized views. The process of selecting materialized views is mainly divided into two phases. The first phase is using Greedy Algorithm to produce the primary materialized views (PV). PV is related with the horizontal granularity level since it is based on the GROUP-BY attributes to generate the materialized views. Based on the result of this phase, we are using Vertical Fragmentation to fragment the PV. The resulting views are called secondary materialized views (SV). This phase is related with the vertical granularity level since it is based on the application properties (such as access patterns and frequencies) to split the relations. As a result, SV have a better benefit than the PV under a given space constraint since it uses the application properties to generate the materialized views. We conduct some preliminary experiments to show that the materialized views generated by these two phases have a better benefit in terms of saving time to storage spaces ratio. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b15176745.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 5.15 MB | Adobe PDF | View/Open |
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