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Topk Nearest Neighbor Search In Uncertain Data Series
"... Many real applications consume data that is intrinsically uncertain, noisy and errorprone. In this study, we investigate the problem of finding the topk nearest neighbors in uncertain data series, which occur in several different domains. We formalize the topk nearest neighbor problem for uncerta ..."
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Cited by 2 (2 self)
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Many real applications consume data that is intrinsically uncertain, noisy and errorprone. In this study, we investigate the problem of finding the topk nearest neighbors in uncertain data series, which occur in several different domains. We formalize the topk nearest neighbor problem
Efficient Search for the Topk Probable Nearest Neighbors in Uncertain Databases ABSTRACT
"... Uncertainty pervades many domains in our lives. Current reallife applications, e.g., location tracking using GPS devices or cell phones, multimedia feature extraction, and sensor data management, deal with different kinds of uncertainty. Finding the nearest neighbor objects to a given query point i ..."
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Cited by 53 (1 self)
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is an important query type in these applications. In this paper, we study the problem of finding objects with the highest marginal probability of being the nearest neighbors to a query object. We adopt a general uncertainty model allowing for data and query uncertainty. Under this model, we define new query
When Is "Nearest Neighbor" Meaningful?
 In Int. Conf. on Database Theory
, 1999
"... . We explore the effect of dimensionality on the "nearest neighbor " problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance ..."
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Cited by 402 (1 self)
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. We explore the effect of dimensionality on the "nearest neighbor " problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches
Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces
, 1993
"... We consider the computational problem of finding nearest neighbors in general metric spaces. Of particular interest are spaces that may not be conveniently embedded or approximated in Euclidian space, or where the dimensionality of a Euclidian representation is very high. Also relevant are highdim ..."
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Cited by 356 (5 self)
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We consider the computational problem of finding nearest neighbors in general metric spaces. Of particular interest are spaces that may not be conveniently embedded or approximated in Euclidian space, or where the dimensionality of a Euclidian representation is very high. Also relevant are high
Continuous Nearest Neighbor Search
, 2002
"... A continuous nearest neighbor query retrieves the nearest neighbor (NN) of every point on a line segment (e.g., "find all my nearest gas stations during my route from point s to point e"). The result contains a set of <point, interval> tuples, such that point is the NN of all po ..."
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Cited by 159 (10 self)
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points in the corresponding interval. Existing methods for continuous nearest neighbor search are based on the repetitive application of simple NN algorithms, which incurs significant overhead. In this paper we propose techniques that solve the problem by performing a single query for the whole
Uncertain Nearest Neighbor Classification
"... This work deals with the problem of classifying uncertain data. With this aim the Uncertain Nearest Neighbor (UNN) rule is here introduced, which represents the generalization of the deterministic nearest neighbor rule to the case in which uncertain objects are available. The UNN rule relies on the ..."
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on properties that greatly reduce the temporal cost associated with nearest neighbor class probability computation. Experimental results are presented, showing that the UNN rule is effective and efficient in classifying uncertain data.
Mtree: An Efficient Access Method for Similarity Search in Metric Spaces
, 1997
"... A new access meth d, called Mtree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion o ..."
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Cited by 652 (38 self)
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of objects and split management, whF h keep th Mtree always balanced  severalheralvFV split alternatives are considered and experimentally evaluated. Algorithd for similarity (range and knearest neigh bors) queries are also described. Results from extensive experimentationwith a prototype system
An Efficient Boosting Algorithm for Combining Preferences
, 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
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Cited by 707 (18 self)
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The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new
L1 topk nearest neighbor searching with uncertain queries
, 2013
"... In this paper, we present algorithms for the topk nearest neighbor searching where the input points are exact and the query point is uncertain under the L1 metric in the plane. The uncertain query point is represented by a discrete probability distribution function, and the goal is to efficiently ..."
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Cited by 1 (1 self)
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In this paper, we present algorithms for the topk nearest neighbor searching where the input points are exact and the query point is uncertain under the L1 metric in the plane. The uncertain query point is represented by a discrete probability distribution function, and the goal is to efficiently
kNearest Neighbors in Uncertain Graphs
"... Complex networks, such as biological, social, and communication networks, often entail uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of similarity search in standard graphs, a fundamental problem for probabilistic graphs is to efficiently answer knearest neig ..."
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Cited by 31 (4 self)
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Complex networks, such as biological, social, and communication networks, often entail uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of similarity search in standard graphs, a fundamental problem for probabilistic graphs is to efficiently answer knearest
Results 1  10
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