Book Chapter10.1007/978-3-540-39984-1_28
Memory-adaptative dynamic spatial approximation trees
Diego Arroyuelo,Francisca Muñoz,Gonzalo Navarro,Nora Susana Reyes +3 more
- 08 Oct 2003
- pp 360-368
TL;DR: This paper combines dynamic spatial approximation trees and pivoting schemes in a data structure that enjoys the features of dsa–trees and that improves query time by making the best use of the available memory.
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Abstract: Dynamic spatial approximation trees (dsa–trees) are efficient data structures for searching metric spaces. However, using enough storage, pivoting schemes beat dsa–trees in any metric space. In this paper we combine both concepts in a data structure that enjoys the features of dsa–trees and that improves query time by making the best use of the available memory. We show experimentally that our data structure is competitive for searching metric spaces.
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Citations
The Basic Principles of Metric Indexing
Magnus Lie Hetland
- 01 Jan 2009
TL;DR: This chapter describes several methods of similarity search, based on metric indexing, in terms of their common, underlying principles, and several approaches to creating lower bounds using the metric axioms are discussed.
Enlarging nodes to improve dynamic spatial approximation trees
Marcelo Barroso,Nora Susana Reyes,Rodrigo Paredes +2 more
- 18 Sep 2010
TL;DR: A new data structure for searching in metric spaces is proposed, based on the DSA--trees, which holds its virtues and takes advantage of element clusters, which are present in many metric spaces, and can also make better use of available memory to improve searches.
Efficient parallelization of spatial approximation trees
Mauricio Marin,Nora Susana Reyes +1 more
- 22 May 2005
TL;DR: This paper describes the parallelization of the Spatial Approximation Tree and proposes a method for load balancing the work performed by the processors, which is self-tuning and is able to dynamically follow changes in the work-load generated by user queries.
Range queries in natural language dictionaries with recursive lists of clusters
Margarida Mamede,Fernanda Barbosa +1 more
- 01 Jan 2007
TL;DR: RLC is the only data structure that always keeps its good performance, whether the space dimension is lower or higher, and whether the query radius is smaller or larger.
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Dynamic Spatial Approximation Trees with clusters for secondary memory
Luis Britos,Alicia Marcela Printista,Nora Susana Reyes +2 more
- 01 Jan 2010
TL;DR: A secondary-memory variant of the Dynamic Spatial Approximation Tree with Clusters (DSACL-tree) which has shown to be competitive in main memory is introduced and the resulting index is a much more practical data structure that can be useful in a wide range of database applications.
References
Searching in metric spaces
TL;DR: A unified view of all the known proposals to organize metric spaces, so as to be able to understand them under a common framework, and presents a quantitative definition of the elusive concept of "intrinsic dimensionality".
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Fully Dynamic Spatial Approximation Trees
Gonzalo Navarro,Nora Susana Reyes +1 more
- 11 Sep 2002
TL;DR: A dynamic version of the sa-tree that handles insertions and deletions is presented, showing experimentally that the price of adding dynamism is rather low and the outcome is a much more practical data structure that can be useful in a wide range of applications.
How to improve the pruning ability of dynamic metric access methods
Caetano Traina,Agma J. M. Traina,Roberto Figueira Santos Filho,Christos Faloutsos +3 more
- 04 Nov 2002
TL;DR: This paper defines a new measurement, called "prunability," which indicates how well a pruning technique carries out the task of cutting down distance calculations at each tree level, and presents a new dynamic access method, aiming to minimize the number of distance calculations required to answer similarity queries.