A Dynamic Pivoting Algorithm Based on Spatial Approximation Indexes

Abstract

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 the query performance adding pivot information. In this paper we extend previous work on dynamic sa-trees with pivots, and show how the pivot information can be used to a full extent to improve the search performance. The result is a technique that allows one to traverse a dynamic sa-tree without necessarily comparing all traversed nodes against the query object. As a result, the novel algorithm makes a much better use of the available space, yielding a saving of distance computations of about 10% to 70%, compared with previous sa-tree schemes that use pivot information.