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191115 ||| eng |
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|a 9783540457701
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| 100 |
1 |
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|a Yu, Cui
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| 245 |
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|a High-Dimensional Indexing
|h Elektronische Ressource
|b Transformational Approaches to High-Dimensional Range and Similarity Searches
|c by Cui Yu
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| 250 |
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|a 1st ed. 2002
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2002, 2002
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| 300 |
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|a XII, 156 p
|b online resource
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| 505 |
0 |
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|a High-Dimensional Indexing -- Indexing the Edges — A Simple and Yet Efficient Approach to High-Dimensional Range Search -- Performance Study of Window Queries -- Indexing the Relative Distance — An Efficient Approach to KNN Search -- Similarity Range and Approximate KNN Searches with iMinMax -- Performance Study of Similarity Queries -- Conclusions
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| 653 |
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|a Artificial intelligence / Data processing
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| 653 |
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|a Multimedia systems
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| 653 |
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|a Information Storage and Retrieval
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| 653 |
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|a Database Management
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| 653 |
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|a Application software
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| 653 |
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|a Information storage and retrieval systems
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| 653 |
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|a Information retrieval
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| 653 |
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|a Computer and Information Systems Applications
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| 653 |
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|a Data Storage Representation
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| 653 |
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|a Multimedia Information Systems
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| 653 |
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|a Database management
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| 653 |
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|a Computer architecture
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| 653 |
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|a Data Science
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
0 |
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|a Lecture Notes in Computer Science
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| 028 |
5 |
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|a 10.1007/3-540-45770-4
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| 856 |
4 |
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|u https://doi.org/10.1007/3-540-45770-4?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 005.74
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| 520 |
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|a 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
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