Enlarging nodes to improve dynamic spatial approximation trees

Abstract

The metric space model allows abstracting many similarity search problems. Similarity search has multiple applications especially in the multimedia databases area. The idea is to index the database so as to accelerate similarity queries. Although there are several promising indices, few of them are dynamic, i.e., once created very few allow to perform insertions and deletions of elements at a reasonable cost.The Dynamic Spatial Approximation Trees (DSA-trees) have shown to be a suitable data structure for searching high dimensional metric spaces or queries with low selectivity (i.e., large radius), and are also completely dynamic. The performance of DSA-trees is directly related to the amount of backtracking in search time. To boost the performance in this data structure a sufficient condition is to maintain in the nodes elements close-to-each-other. In this work we propose a new data structure for searching in metric spaces, based on the DSA-tree, 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. In fact, we use these element clusters to improve the spatial approximation.

Skewness‐aware clustering tree for unevenly distributed spatial sensor nodes in smart city

“…In the past 30 years, quite a few techniques have been developed for constructing spatial index , but there is no consensus on the best spatial index structure suitable for most situations . Most works are the extensions of R‐tree and focus on reducing the overlap and coverage in order to simplify the query path.…”