Indexing techniques for metric databases with costly searches

Similarity search in database systems is becoming an increasingly important task in modern application domains such as artificial intelligence, computational biology, pattern recognition and data mining. With the evolution of information, applications with new data types such as text, images, videos, audio, DNA and protein sequences have began to appear. Despite extensive research and the development of a plethora of index structures, similarity search is still too costly in many application domains, especially when measuring the similarity between a pair or objects is expensive. In this dissertation, the similarity search queries we consider are classified under similarity search and similarity join queries. Several new indexing techniques to improve the performance of similarity search are proposed. For the similarity search queries, reference-based indexing methods applicable to both static and growing databases are proposed. For similarity join queries, a generalized nearest neighbor framework and several search and optimization algorithms are proposed. The extensive experiments evaluates the different parameters used by the proposed methods and performance improvements over the state-of-art algorithms.