ACM Computing Surveys (CSUR)
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd 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
D-Index: Distance Searching Index for Metric Data Sets
Multimedia Tools and Applications
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Dynamic spatial approximation trees
Journal of Experimental Algorithmics (JEA)
An access structure for similarity search in metric spaces
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Analyzing Metric Space Indexes: What For?
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Fully dynamic metric access methods based on hyperplane partitioning
Information Systems
Sparse spatial selection for novelty-based search result diversification
SPIRE'11 Proceedings of the 18th international conference on String processing and information retrieval
DSACL+-tree: a dynamic data structure for similarity search in secondary memory
SISAP'12 Proceedings of the 5th international conference on Similarity Search and Applications
Modelling efficient novelty-based search result diversification in metric spaces
Journal of Discrete Algorithms
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Metric space searching is an emerging technique to address the problem of efficient similarity searching in many applications, including multimedia databases and other repositories handling complex objects. Although promising, the metric space approach is still immature in several aspects that are well established in traditional databases. In particular, most indexing schemes are not dynamic, that is, few of them tolerate insertion of elements at reasonable cost over an existing index and only a few work efficiently in secondary memory. In this paper we introduce a secondary-memory variant of the Dynamic Spatial Approximation Tree, which has shown to be competitive in main memory. The resulting index handles well the secondary memory scenario and is competitive with the state of the art, becoming a useful alternative in a wide range of database applications. Moreover, our ideas are applicable to other secondary-memory trees where there is little control over the tree shape.