Improved Deletions in Dynamic Spatial Approximation Trees

Fully Dynamic and Memory-Adaptative Spatial Approximation Trees

Hybrid dynamic spatial approximation trees are recently proposed data structures for searching in metric spaces, based on combining the concepts of spatial approximation and pivot based algorithms. These data structures are hybrid schemes, with the full features of dynamic spatial approximation trees and able of using the available memory to improve the query time. It has been shown that they c...

Fully Dynamic Spatial Approximation Trees

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 b...

Improved Dynami Spatial Approximation Trees

A Dynamic Pivoting Algorithm Based on Spatial Approximation Indexes

Metric indexes aim at reducing the amount of distance evaluations carried out when searching a metric space. Spatial approximation trees (sa-trees for short), in particular, are efficient data structures, which have shown to be competitive in metric spaces of medium to high difficulty, or queries with low selectivity. Sa-trees can be also made dynamic, and can use the available space to improve...

Q0-tree: a Dynamic Structure for Accessing Spatial Objects with Arbitrary Shapes

The items in a spatial database have location, extent, and shape with respect to a spatial coordinate system. Simple approximations to these attributes, say by bounding rectangles, are storage efficient and easy to manipulate. But effective spatial retrieval (on either location, extent, or shape) require a more precise representation of these attributes. In this report, we describe a highly com...