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- catalog abstract "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.".
- catalog contributor b12685920.
- catalog created "2002.".
- catalog date "2002".
- catalog date "2002.".
- catalog dateCopyrighted "2002.".
- catalog description "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.".
- catalog description "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.".
- catalog extent "xi, 150 p. :".
- catalog identifier "3540441999 (softcover : alk. paper)".
- catalog isPartOf "Lecture notes in computer science ; 2341".
- catalog issued "2002".
- catalog issued "2002.".
- catalog language "eng".
- catalog publisher "Berlin ; New York : Springer,".
- catalog subject "005.74 21".
- catalog subject "Computer science.".
- catalog subject "Data structures (Computer science).".
- catalog subject "Database management.".
- catalog subject "Indexing.".
- catalog subject "Information storage and retrieval systems.".
- catalog subject "Information systems.".
- catalog subject "Multimedia systems.".
- catalog subject "QA76.9.D3 Y83 2002".
- catalog tableOfContents "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.".
- catalog title "High-dimensional indexing : transformational approaches to high-dimensional range and similarity searches / Cui Yu.".
- catalog type "text".