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List of Figures List of Tables Preface Acknowledgments
Part I Fundamental Issues
1. SPATIAL DATABASE CONCEPTS
1 Introduction
2 Spatial Query Processing
3 Access Methods
4 Handling High-Dimensional Data
5 Spatial Data Support in Commercial Systems
6 Summary
7 Further Reading
2. THE R-TREE AND VARIATIONS
1 Introduction
2 The Original R-tree
3 Dynamic R-tree Variants
3.1 The R+-tree
3.2 The R*-tree
3.3 The Hilbert R-tree
4 Static R-tree Variants
4.1 The Packed R-tree
4.2 The Hilbert Packed R-tree
4.3 The STR Packed R-tree
5. Performance Issues
6. R-trees in Emerging Applications
7. Summary
8. Further Reading
Part II Nearest Neighbor Search in Spatial and Spatiotemporal Databases
3. NEAREST NEIGHBOR QUERIES
1 Introduction
2 The Nearest Neighbor Problem
3 Applications
4 Nearest Neighbor Queries in R-trees
5 Nearest Neighbor Queries in Multimedia Applications
6 Summary
7 Further Reading
4. ANALYSIS OF NEAREST NEIGHBOR QUERIES
1 Introduction
2 Analytical Considerations
2.1 Preliminaries
2.2 Estimation of dnn and dm
2.3 Performance Estimation
3 Performance Evaluation
3.1 Preliminaries
3.2 Experimental Results
4 Summary
5 Further Reading
5. NEAREST NEIGHBOR QUERIES IN MOVING OBJECTS
1 Introduction
2 Organizing Moving Objects
3 Nearest Neighbor Queries
3. 1 The NNS Algorithm
3.1 Algorithm NNS-a
3.1.2 Algorithm NNS-b
3.2 Query Processing with TPR-trees
4 Performance Evaluation
4.1 Preliminaries
4.2 Experimental Results
5 Summary
6 Further Reading
Part III Nearest Neighbor Search with Multiple Resources
6. PARALLEL AND DISTRIBUTED DATABASES
1 Introduction
2 Multidisk Systems
3 Multiprocessor Systems
4 Distributed Systems
5 Summary
6 Further Reading
7 MULTIDISK QUERY PROCESSING
1 Introduction
2 Algorithms
2.1 The Branch-and-Bound Algorithm
2.2 Full-Paral1el Similarity Search
2.3 Candidate Reduction Similarity Search
2.4 Optimal Similarity Search
3 Performance Evaluation
3.1 Preliminaries
3.2 Experimental Results
3.3 Interpretation of Results
4 Summary
5 Further Reading
8. MULTIPROCESSOR QUERY PROCESSING
1 Introduction
2 Performance Estimation
3 Parallel Algorithms
3.1 Adapting BB-NNF in Declustered R-trees
3.2 The Parallel Nearest Neighbor Finding (P-NNF) Method
3.3 When Statistics are not Available
3.4 Correctness of P-NNF Algorithms
4 Performance Evaluation
4.1 Preliminaries
4.2 The Cost Model
4.3 Experimental Results
4.4 Interpretation of Results
5 Summary
6 Further Reading
9. DISTRIBUTED QUERY PROCESSING
1 Introduction
2 Query Evaluation Strategies
2.1 Algorithms
2.2 Theoretical Study
2.3 Analytical Comparison
3 The Impact of Derived Data
4 Performance Evaluation
4.1 Preliminaries
4.2 Cost Model Evaluation
4.3 Experimental Results
5 Discussion
6 Summary
7 Further Reading Epilogue References
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Add Nearest Neighbor Search:: A Database Perspective, Modern applications are both data and computationally intensive and require the storage and manipulation of voluminous traditional (alphanumeric) and nontraditional data sets (images, text, geometric objects, time-series). Examples of such emerging applic, Nearest Neighbor Search:: A Database Perspective to the inventory that you are selling on WonderClubX
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Add Nearest Neighbor Search:: A Database Perspective, Modern applications are both data and computationally intensive and require the storage and manipulation of voluminous traditional (alphanumeric) and nontraditional data sets (images, text, geometric objects, time-series). Examples of such emerging applic, Nearest Neighbor Search:: A Database Perspective to your collection on WonderClub |