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» Maximal Vector Computation in Large Data Sets
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DAWAK
2003
Springer
14 years 2 months ago
Handling Large Workloads by Profiling and Clustering
View materialization is recognized to be one of the most effective ways to increase the Data Warehouse performance; nevertheless, due to the computational complexity of the techniq...
Matteo Golfarelli
ICML
2008
IEEE
14 years 10 months ago
Fully distributed EM for very large datasets
In EM and related algorithms, E-step computations distribute easily, because data items are independent given parameters. For very large data sets, however, even storing all of th...
Jason Wolfe, Aria Haghighi, Dan Klein
APVIS
2007
13 years 10 months ago
A comparison of vertex ordering algorithms for large graph visualization
In this study, we examine the use of graph ordering algorithms for visual analysis of data sets using visual similarity matrices. Visual similarity matrices display the relationsh...
Christopher Mueller, Benjamin Martin, Andrew Lumsd...
SIGMOD
2004
ACM
92views Database» more  SIGMOD 2004»
14 years 9 months ago
Online Maintenance of Very Large Random Samples
Random sampling is one of the most fundamental data management tools available. However, most current research involving sampling considers the problem of how to use a sample, and...
Chris Jermaine, Abhijit Pol, Subramanian Arumugam
CVPR
2009
IEEE
15 years 4 months ago
Active Learning for Large Multi-class Problems
Scarcity and infeasibility of human supervision for large scale multi-class classification problems necessitates active learning. Unfortunately, existing active learning methods ...
Prateek Jain (University of Texas at Austin), Ashi...