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|Title: ||QUERY OPTIMIZATION HEURISTICS FOR CONTENT-BASED IMAGE DATABASE|
|Authors: ||SEIFU, GELETA|
|Advisors: ||DR. SOLOMON ATNAFU|
|Copyright: ||2004 |
|Date Added: ||27-Aug-2008 |
|Publisher: ||Addis Ababa University|
|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.|
|Description: ||A Thesis
Submitted to the School of Graduate Studies of Addis Ababa University in
partial fulfillment of the requirements for the Degree of Master of Science
in Computer Science|
|Appears in:||Thesis - Computer Science|
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