New deletion method for dynamic spatial approximation trees

Chilean Computer Science Society, …, 2003

The Dynamic Spatial Approximation Tree (dsa-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. The dsa-tree supports insertion and deletions of elements. However, it has been noted that deletions degrade the structure over time, so the structure cannot be regarded as fully dynamic in the sense that deletions are not sustainable for long periods of time.

Journal of Computer Science Technology, 2014

Metric space searching is an emerging technique to address the problem of similarity searching in many applications. In order to efficiently answer similarity queries, the database must be indexed. In some interesting real applications dynamism is an indispensable property of the index. There are very few actually dynamic indexes that support not only searches, but also insertions and deletions of elements. The dynamic spatial approximation tree (DSAT) is a data structure specially designed for searching in metric spaces, which compares favorably against other data structures in high dimensional spaces or queries with low selectivity. Insertions are efficient and easily supported in DSAT, but deletions degrade the structure over time. Several methods are proposed to handle deletions over the DSAT. One of them has shown to be superior to the others, in the sense that it permits controlling the expected deletion cost as a proportion of the insertion cost and searches does not overly degrade after several deletions. In this paper we propose and study a new alternative deletion method, based on the better existing strategy. The outcome is a fully dynamic data structure that can be managed through insertions and deletions over arbitrarily long periods of time without any significant reorganization.

String Processing and Information Retrieval, 2002

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 being a static data structure, that is, once built, one cannot add or delete elements. These facts rule it out for many interesting applications. In this paper we overcome these weaknesses. We present a dynamic version of the sa-tree that handles insertions and deletions, showing experimentally that the price of adding dynamism is rather low. This is remarkable by itself since very few data structures for metric spaces are fully dynamic. In addition, we show how to obtain large improvements in construction and search time for low dimensional spaces or highly selective queries. The outcome is a much more practical data structure that can be useful in a wide range of applications.

2001

The spatial approximation tree (sa-tree) is a recently proposed data structure for searching in metric spaces. It has been shown to compare favorably against alternative data structures in spaces of high dimension or queries with low selectivity. The main drawback of the sa-tree is that it is a static data structure, that is, once built, it is difficult to add new elements to it. This rules it out for many interesting applications. In this paper we overcome this weakness. We propose and study several methods to handle insertions in the sa-tree. Some are classical solutions well known in the data structures community, while the most promising ones have been specifically developed considering the particular properties of the sa-tree, and involve new algorithmic insights in the behavior of this data structure. As a result, we show that it is viable to modify the sa-tree so as to permit fast insertions while keeping its good search efficiency

2010

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.

Similarity Search and Applications, 2009 …, 2009

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.

2002

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. The main drawback of the ...

String Processing and …, 2003

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.

2003

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 compare favorably against alternative data structures in spaces of medium difficulty. In this paper we complete and improve hybrid dynamic spatial approximation trees, by presenting a new search alternative, an algorithm to remove objects from the tree, and an improved way of managing the available memory. The result is a fully dynamic and optimized data structure for similarity searching in metric spaces.

2003

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.