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» Maximally joining probabilistic data
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VLDB
2005
ACM
93views Database» more  VLDB 2005»
14 years 4 months ago
Revisiting Pipelined Parallelism in Multi-Join Query Processing
Multi-join queries are the core of any integration service that integrates data from multiple distributed data sources. Due to the large number of data sources and possibly high v...
Bin Liu, Elke A. Rundensteiner
ICDE
2009
IEEE
157views Database» more  ICDE 2009»
15 years 17 days ago
Confidence-Aware Join Algorithms
In uncertain and probabilistic databases, confidence values (or probabilities) are associated with each data item. Confidence values are assigned to query results based on combinin...
Parag Agrawal, Jennifer Widom
EDBT
2008
ACM
103views Database» more  EDBT 2008»
14 years 11 months ago
A stratified approach to progressive approximate joins
Users often do not require a complete answer to their query but rather only a sample. They expect the sample to be either the largest possible or the most representative (or both)...
Wee Hyong Tok, Stéphane Bressan, Mong-Li Le...
DBPL
2009
Springer
144views Database» more  DBPL 2009»
14 years 5 months ago
General Database Statistics Using Entropy Maximization
Abstract. We propose a framework in which query sizes can be estimated from arbitrary statistical assertions on the data. In its most general form, a statistical assertion states t...
Raghav Kaushik, Christopher Ré, Dan Suciu
ICIP
2010
IEEE
13 years 8 months ago
Disparity and normal estimation through alternating maximization
In this paper, we propose an algorithm that recovers binocular disparities in accordance with the surface properties of the scene under consideration. To do so, we estimate the di...
Ramya Narasimha, Elise Arnaud, Florence Forbes, Ra...