In this monograph, we study the problem of high-dimensional indexing and systematically introduce two efficient index structures: one for range queries and the other for similarity queries. Extensive experiments and comparison studies are conducted to demonstrate the superiority of the proposed indexing methods.Many new database applications, such as multimedia databases or stock price information systems, transform important features or properties of data objects into high-dimensional points. Searching for objects based on these features is thus a search of points in this feature space. To support efficient retrieval in such high-dimensional databases, indexes are required to prune the search space. Indexes for low-dimensional databases are well studied, whereas most of these application specific indexes are not scaleable with the number of dimensions, and they are not designed to support similarity searches and high-dimensional joins.
Lecture Notes in Computer Science Edited by G. Goos, J. Hartmanis, and J. van Leeuwen 2341 3 Berlin Heidelberg New York Barcelona Hong Kong London Milan Paris Tokyo Cui Yu High-Dimensional Indexing Transformational Approaches to High-Dimensional Range and Similarity Searches 13 Series Editors Gerhard Goos, Karlsruhe University, Germany Juris Hartmanis, Cornell University, NY, USA Jan van Leeuwen, Utrecht University, The Netherlands Author Cui Yu Monmouth University, Department of Computer Science West Long Branch, NJ 07764, USA National University of Singapore, Department of Computer Science Kent Ridge, Singapore 117543, Singapore E-mail:
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