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Constrained Nearest Neighbor Queries
 in SSTD
, 2001
"... In this paper we introduce the notion of constrained nearest neighbor queries (CNN) and propose a series of methods to answer them. This class of queries can be thought of as nearest neighbor queries with range constraints. Although both nearest neighbor and range queries have been analyzed exten ..."
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Cited by 44 (4 self)
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In this paper we introduce the notion of constrained nearest neighbor queries (CNN) and propose a series of methods to answer them. This class of queries can be thought of as nearest neighbor queries with range constraints. Although both nearest neighbor and range queries have been analyzed
Probabilistic Verifiers: Evaluating Constrained NearestNeighbor Queries over Uncertain Data
"... Abstract â€” In applications like locationbased services, sensor monitoring and biological databases, the values of the database items are inherently uncertain in nature. An important query for uncertain objects is the Probabilistic NearestNeighbor Query (PNN), which computes the probability of each ..."
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Cited by 55 (7 self)
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the exact probability values. For example, he may only need answers that have sufficiently high confidence. We thus propose the Constrained NearestNeighbor Query (CPNN), which returns the IDs of objects whose probabilities are higher than some threshold, with a given error bound in the answers. The C
Probabilistic Verifiers: Evaluating Constrained NearestNeighbor Queries over Uncertain Data
"... Abstractâ€”In applications like locationbased services, sensor monitoring and biological databases, the values of the database items are inherently uncertain in nature. An important query for uncertain objects is the Probabilistic NearestNeighbor Query (PNN), which computes the probability of each o ..."
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the exact probability values. For example, he may only need answers that have sufficiently high confidence. We thus propose the Constrained NearestNeighbor Query (CPNN), which returns the IDs of objects whose probabilities are higher than some threshold, with a given error bound in the answers. The C
Probabilistic Verifiers: Evaluating Constrained NearestNeighbor Queries over Uncertain Data
"... Abstract In applications like locationbased services, sensor monitoring and biological databases, the values of the database items are inherently uncertain in nature. An important query for uncertain objects is the Probabilistic NearestNeighbor Query (PNN), which computes the probability of each ..."
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the exact probability values. For example, he may only need answers that have sufficiently high confidence. We thus propose the Constrained NearestNeighbor Query (CPNN), which returns the IDs of objects whose probabilities are higher than some threshold, with a given error bound in the answers. The C
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
Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality
, 1998
"... The nearest neighbor problem is the following: Given a set of n points P = fp 1 ; : : : ; png in some metric space X, preprocess P so as to efficiently answer queries which require finding the point in P closest to a query point q 2 X. We focus on the particularly interesting case of the ddimens ..."
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Cited by 1017 (40 self)
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The nearest neighbor problem is the following: Given a set of n points P = fp 1 ; : : : ; png in some metric space X, preprocess P so as to efficiently answer queries which require finding the point in P closest to a query point q 2 X. We focus on the particularly interesting case of the d
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
The SRtree: An Index Structure for HighDimensional Nearest Neighbor Queries
, 1997
"... Recently, similarity queries on feature vectors have been widely used to perform contentbased retrieval of images. To apply this technique to large databases, it is required to develop multidimensional index structures supporting nearest neighbor queries e ciently. The SStree had been proposed for ..."
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Cited by 442 (3 self)
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Recently, similarity queries on feature vectors have been widely used to perform contentbased retrieval of images. To apply this technique to large databases, it is required to develop multidimensional index structures supporting nearest neighbor queries e ciently. The SStree had been proposed
Coordination of Groups of Mobile Autonomous Agents Using Nearest Neighbor Rules
, 2002
"... In a recent Physical Review Letters paper, Vicsek et. al. propose a simple but compelling discretetime model of n autonomous agents fi.e., points or particlesg all moving in the plane with the same speed but with dierent headings. Each agent's heading is updated using a local rule based on ..."
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Cited by 1245 (60 self)
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on the average of its own heading plus the headings of its \neighbors." In their paper, Vicsek et. al. provide simulation results which demonstrate that the nearest neighbor rule they are studying can cause all agents to eventually move in the same direction despite the absence of centralized
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
Results 1  10
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