Many new database applications, such as multimedia databases or sk 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.
Many new database applications, such as multimedia databases or sk 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.
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High-Dimensional Indexing: Transformational Approaches to High-Dimensional Range and Similarity Searches
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High-Dimensional Indexing: Transformational Approaches to High-Dimensional Range and Similarity Searches
156Paperback(2002)
Product Details
ISBN-13: | 9783540441991 |
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Publisher: | Springer Berlin Heidelberg |
Publication date: | 12/16/2002 |
Series: | Lecture Notes in Computer Science , #2341 |
Edition description: | 2002 |
Pages: | 156 |
Product dimensions: | 6.10(w) x 9.17(h) x (d) |