Multidimensional access methods
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
Searching in Metric Spaces by Spatial Approximation
SPIRE '99 Proceedings of the String Processing and Information Retrieval Symposium & International Workshop on Groupware
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)
Dynamic spatial approximation trees
Journal of Experimental Algorithmics (JEA)
Searching and Updating Metric Space Databases Using the Parallel EGNAT
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Sparse spatial selection for novelty-based search result diversification
SPIRE'11 Proceedings of the 18th international conference on String processing and information retrieval
Recursive lists of clusters: a dynamic data structure for range queries in metric spaces
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
An index data structure for searching in metric space databases
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Efficient parallelization of spatial approximation trees
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
A log square average case algorithm to make insertions in fast similarity search
Pattern Recognition Letters
Modelling efficient novelty-based search result diversification in metric spaces
Journal of Discrete Algorithms
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The Spatial Approximation Tree (sa-tree) is a recently proposed data structure for searching in metric spaces. It has been shown that it compares favorably against alternative data structures in spaces of high dimension or queries with low selectivity. Its main drawbacks are: costly construction time, poor performance in low dimensional spaces or queries with high selectivity, and the fact of being a static data structure, that is, once built, one cannot add or delete elements. These facts rule it out for many interesting applications.In this paper we overcome these weaknesses. We present a dynamic version of the sa-tree that handles insertions and deletions, showing experimentally that the price of adding dynamism is rather low. This is remarkable by itself since very few data structures for metric spaces are fully dynamic. In addition, we show how to obtain large improvements in construction and search time for low dimensional spaces or highly selective queries. The outcome is a much more practical data structure that can be useful in a wide range of applications.