ACM Computing Surveys (CSUR)
Fixed Queries Array: A Fast and Economical Data Structure for Proximity Searching
Multimedia Tools and Applications
Fully Dynamic Spatial Approximation Trees
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
Improved search heuristics for the sa-tree
Pattern Recognition Letters
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
Fast Approximate Similarity Search in Extremely High-Dimensional Data Sets
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Dynamic spatial approximation trees
Journal of Experimental Algorithmics (JEA)
Parallel query processing on distributed clustering indexes
Journal of Discrete Algorithms
Efficient skyline computation in metric space
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Combinatorial Framework for Similarity Search
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Dimension reduction for distance-based indexing
Proceedings of the Third International Conference on SImilarity Search and APplications
Combining elimination rules in tree-based nearest neighbor search algorithms
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Pivot selection: Dimension reduction for distance-based indexing
Journal of Discrete Algorithms
Efficient similarity search in metric spaces with cluster reduction
SISAP'12 Proceedings of the 5th international conference on Similarity Search and Applications
SISAP'12 Proceedings of the 5th international conference on Similarity Search and Applications
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We propose a new data structure to search in metric spaces. A metric space is formed by a collection of objects and a distance function defined among them, which satisfies the triangular inequality. The goal is, given a set of objects and a query, retrieve those objects close enough to the query. The number of distances computed to achieve this goal is the complexity measure.Our data structure, called sa-tree (``spatial approximation tree''), is based on approaching spatially the searched objects. We analyze our method and show that the number of distance evaluations to search among n objects is o(n). We show experimentally that the sa-tree is the best existing technique when the metric space is high-dimensional or the query has low selectivity. These are the most difficult cases in real applications.