Multidimensional Data Structures

Samet, Hanan

doi:10.1201/9781420049503-c19

Cited by 197 publications

(341 citation statements)

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“…Since, the computational cost of determining the similarity among objects is known to be a significant part of the total running time, a number of data structures and algorithms have been devised to deal efficiently with large collections of data [13,12,5]. Given a database of n = |S| objects, queries can be trivially answered by performing n distance evaluations.…”

Section: Nearest Neighbor Query (K-nn)mentioning

confidence: 99%

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Enlarging nodes to improve dynamic spatial approximation trees

Barroso

Reyes

Paredes

2010

Proceedings of the Third International Conference on SImilarity Search and APplications

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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.

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Section: Nearest Neighbor Query (K-nn)mentioning

confidence: 99%

“…However, there are algorithms that combine ideas from both areas. See [12,13,5,9] for more complete surveys.…”

Section: Previous Workmentioning

confidence: 99%

Enlarging nodes to improve dynamic spatial approximation trees

Barroso

Reyes

Paredes

2010

Proceedings of the Third International Conference on SImilarity Search and APplications

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“…Conventionally, data analysis approaches, such as traditional statistics or OLAP techniques [8], assume a relatively simple multi-dimensional model [7] (simple with respect to the separate data dimensions). For complex data sets it is necessary to provide an adequate data model.…”

Section: Related Workmentioning

confidence: 99%

Evolutional Insights from UML and Source Code Versions using Information Visualization and Visual Analysis

Bohner

Gračanin

Henry

et al. 2007

2007 4th IEEE International Workshop on Visualizing Software for Understanding and Analysis

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“…Lately, to solve similarity search problems for large-scale data sets, approximate search methods that guarantee some resultant accuracy have been studied with considerable effort, which contain those based on a tree-type index [4] and locality-sensitive hashing (LSH) family [5]- [7]. In contrast, exact methods have received interest for a long time in the application domains where a data set has relatively low intrinsic dimensionality [3]. Their acceleration is also required for the accuracy evaluation of the approximate and the heuristic methods since an exact search result is necessary as the ground truth.…”

Section: Introductionmentioning

confidence: 99%

“…Developing similarity search methods has become a more important challenge along with the increasing volume of data which we deal with [2], [3]. From a perspective of resultant search accuracy, similarity search methods are classified into three main categories of exact, approximate, and heuristic search.…”

Section: Introductionmentioning

confidence: 99%

Efficient Similarity Search with a Pivot-Based Complete Binary Tree

Yamagishi

Aoyama²,

Saito

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper presents an efficient similarity search method utilizing as an index a complete binary tree (CBT) based on optimized pivots for a large-scale and high-dimensional data set. A similarity search method, in general, requires high-speed performance on both index construction off-line and similarity search itself online. To fulfill the requirement, we introduce novel techniques into an index construction and a similarity search algorithm in the proposed method for a range query. The index construction algorithm recursively employs the following two main functions, resulting in a CBT index. One is a pivot generation function that obtains one effective pivot at each node by efficiently maximizing a defined objective function. The other is a node bisection function that partitions a set of objects at a node into two almost equal-sized subsets based on the optimized pivot. The similarity search algorithm employs a three-stage process that narrows down candidate objects within a given range by pruning unnecessary branches and filtering objects in each stage. Experimental results on one million real image data set with high dimensionality demonstrate that the proposed method finds an exact solution for a range query at around one-quarter to half of the computational cost of one of the state-ofthe-art methods, by using a CBT index constructed off-line at a reasonable computational cost.

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Multidimensional Data Structures

Cited by 197 publications

References 0 publications

Enlarging nodes to improve dynamic spatial approximation trees

Enlarging nodes to improve dynamic spatial approximation trees

Evolutional Insights from UML and Source Code Versions using Information Visualization and Visual Analysis

Efficient Similarity Search with a Pivot-Based Complete Binary Tree

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