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Optimizing AllNearestNeighbor Queries with Trigonometric Pruning
"... Abstract. Many applications require to determine the knearest neighbors for multiple query points simultaneously. This task is known as all(k)nearestneighbor (AkNN) query. In this paper, we suggest a new method for efficient AkNN query processing which is based on spherical approximations for in ..."
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Cited by 5 (0 self)
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Abstract. Many applications require to determine the knearest neighbors for multiple query points simultaneously. This task is known as all(k)nearestneighbor (AkNN) query. In this paper, we suggest a new method for efficient AkNN query processing which is based on spherical approximations
Nearest Neighbor Queries
, 1995
"... A frequently encountered type of query in Geographic Information Systems is to find the k nearest neighbor objects to a given point in space. Processing such queries requires substantially different search algorithms than those for location or range queries. In this paper we present an efficient bra ..."
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Cited by 594 (1 self)
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A frequently encountered type of query in Geographic Information Systems is to find the k nearest neighbor objects to a given point in space. Processing such queries requires substantially different search algorithms than those for location or range queries. In this paper we present an efficient
Efficient Evaluation of AllNearestNeighbor Queries
 Proc. IEEE Intâ€™l Conf. Data Eng. (ICDE
, 2007
"... The All Nearest Neighbor (ANN) operation is a commonly used primitive for analyzing large multidimensional datasets. Since computing ANN is very expensive, in previous works R*tree based methods have been proposed to speed up this computation. These traditional indexbased methods use a pruning me ..."
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Cited by 25 (1 self)
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The All Nearest Neighbor (ANN) operation is a commonly used primitive for analyzing large multidimensional datasets. Since computing ANN is very expensive, in previous works R*tree based methods have been proposed to speed up this computation. These traditional indexbased methods use a pruning
Allnearestneighbors queries in spatial databases
 Proc. 16th International Conf. Scientific and Statistical Databases (SSDBM 2004). Santorini, Greece
, 2004
"... Given two sets A and B of multidimensional objects, the allnearestneighbors (ANN) query retrieves for each object in A its nearest neighbor in B. Although this operation is common in several applications, it has not received much attention in the database literature. In this paper we study alterna ..."
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Cited by 22 (1 self)
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Given two sets A and B of multidimensional objects, the allnearestneighbors (ANN) query retrieves for each object in A its nearest neighbor in B. Although this operation is common in several applications, it has not received much attention in the database literature. In this paper we study
Efficient Evaluation of AllNearestNeighbor Queries
"... The All Nearest Neighbor (ANN) operation is a commonly used primitive for analyzing large multidimensional datasets. Since computing ANN is very expensive, in previous works R*tree based methods have been proposed to speed up this computation. These traditional indexbased methods use a pruning me ..."
Abstract
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The All Nearest Neighbor (ANN) operation is a commonly used primitive for analyzing large multidimensional datasets. Since computing ANN is very expensive, in previous works R*tree based methods have been proposed to speed up this computation. These traditional indexbased methods use a pruning
An Optimal Algorithm for Approximate Nearest Neighbor Searching in Fixed Dimensions
 ACMSIAM SYMPOSIUM ON DISCRETE ALGORITHMS
, 1994
"... Consider a set S of n data points in real ddimensional space, R d , where distances are measured using any Minkowski metric. In nearest neighbor searching we preprocess S into a data structure, so that given any query point q 2 R d , the closest point of S to q can be reported quickly. Given any po ..."
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Cited by 983 (32 self)
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Consider a set S of n data points in real ddimensional space, R d , where distances are measured using any Minkowski metric. In nearest neighbor searching we preprocess S into a data structure, so that given any query point q 2 R d , the closest point of S to q can be reported quickly. Given any
Efficient and Effective Querying by Image Content
 Journal of Intelligent Information Systems
, 1994
"... In the QBIC (Query By Image Content) project we are studying methods to query large online image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include med ..."
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Cited by 500 (13 self)
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In the QBIC (Query By Image Content) project we are studying methods to query large online image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include
Nearoptimal hashing algorithms for approximate nearest neighbor in high dimensions
, 2008
"... In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query. The ..."
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Cited by 443 (7 self)
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In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query
Fast approximate nearest neighbors with automatic algorithm configuration
 In VISAPP International Conference on Computer Vision Theory and Applications
, 2009
"... nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these highdimensional problems ..."
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Cited by 448 (2 self)
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nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these high
Tinydb: An acquisitional query processing system for sensor networks
 ACM Trans. Database Syst
, 2005
"... We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acq ..."
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Cited by 609 (8 self)
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of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination
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