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CORR
2011
Springer
175views Education» more  CORR 2011»
13 years 1 months ago
A Novel Probabilistic Pruning Approach to Speed Up Similarity Queries in Uncertain Databases
Abstract— In this paper, we propose a novel, effective and efficient probabilistic pruning criterion for probabilistic similarity queries on uncertain data. Our approach support...
Thomas Bernecker, Tobias Emrich, Hans-Peter Kriege...
SIGMOD
2007
ACM
176views Database» more  SIGMOD 2007»
14 years 6 months ago
URank: formulation and efficient evaluation of top-k queries in uncertain databases
Top-k processing in uncertain databases is semantically and computationally different from traditional top-k processing. The interplay between query scores and data uncertainty ma...
Mohamed A. Soliman, Ihab F. Ilyas, Kevin Chen-Chua...
ICDE
2007
IEEE
138views Database» more  ICDE 2007»
14 years 7 months ago
Representing and Querying Correlated Tuples in Probabilistic Databases
Probabilistic databases have received considerable attention recently due to the need for storing uncertain data produced by many real world applications. The widespread use of pr...
Prithviraj Sen, Amol Deshpande
ICDE
2009
IEEE
155views Database» more  ICDE 2009»
14 years 1 months ago
SPROUT: Lazy vs. Eager Query Plans for Tuple-Independent Probabilistic Databases
— A paramount challenge in probabilistic databases is the scalable computation of confidences of tuples in query results. This paper introduces an efficient secondary-storage o...
Dan Olteanu, Jiewen Huang, Christoph Koch
VLDB
1999
ACM
127views Database» more  VLDB 1999»
13 years 10 months ago
Evaluating Top-k Selection Queries
In many applications, users specify target values for certain attributes, without requiring exact matches to these values in return. Instead, the result to such queries is typical...
Surajit Chaudhuri, Luis Gravano