Query Optimization Heuristics for Content-Based Image Database
| dc.contributor.advisor | Atnafu, Solomon(PhD) | |
| dc.contributor.author | Geleta, Seifu | |
| dc.date.accessioned | 2018-06-25T08:42:47Z | |
| dc.date.accessioned | 2023-11-29T04:05:48Z | |
| dc.date.available | 2018-06-25T08:42:47Z | |
| dc.date.available | 2023-11-29T04:05:48Z | |
| dc.date.issued | 2004-06 | |
| dc.description.abstract | The goal of query optimization is to select an efficient query evaluation strategy. Query optimization is a widely studied problem in the context of traditional database systems. As a result many query optimization techniques have been developed and integrated into the query processing module of these database systems. Due to the differences in query processing strategy followed by content-based image database systems, query optimization in these systems is different. In recent years, the number of image used in different application areas is increasing at a high rate. As a result, many content-based image retrieval systems and content-based image database systems have been developed. These systems have become important to manage image queries based on low level features when the size of images to be managed is small. However, as the size of image collection to be managed increases, these systems could not be efficient. This is because the underlying database do not provide query optimization scheme for similarity-based image queries. In this thesis we developed heuristic-based query optimization techniques that transform declarative query posed on content-based image database into more efficient form. To test the performance of the proposed optimization techniques we conducted experiments. The result of our experimental tests showed that our optimization techniques have brought a significant cost saving by reducing execution time of queries. In addition to the query optimization techniques, we proposed a method of integrating these query optimization techniques with existing content-based image database systems. As the query optimizer of commercial database systems that support content-based image processing are not fully extensible, we proposed to develop and integrate a query optimizer into the Image query processing module previously proposed. This requires the development of query optimizer that implements the optimization techniques. Hence, we have identified the requirement of query optimizer for content-based image database and presented the design of an extensible rule-based query optimizer. Keywords: Query optimization, image database, similarity-based query processing, Optimization for content-based image database. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/3093 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Query Optimization | en_US |
| dc.subject | Image Database | en_US |
| dc.subject | Similarity-Based Query Processing | en_US |
| dc.subject | Optimization for Content-Based Image Database | en_US |
| dc.title | Query Optimization Heuristics for Content-Based Image Database | en_US |
| dc.type | Thesis | en_US |