High-Dimensional Indexing Transformational Approaches to High-Dimensional Range and Similarity Searches /

• Κύριος συγγραφέας: Yu, Cui (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2002.
• Έκδοση: 1st ed. 2002.
• Σειρά: Lecture Notes in Computer Science, 2341
• Θέματα: Database management.; Information storage and retrieval.; Multimedia information systems.; Application software.; Data structures (Computer science).; Database Management.; Information Storage and Retrieval.; Multimedia Information Systems.; Information Systems Applications (incl. Internet).; Data Storage Representation.; Data Structures.

 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. 

 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.