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ICDE
2003
IEEE
116views Database» more  ICDE 2003»
14 years 6 months ago
Joining Massive High-Dimensional Datasets
We consider the problem of joining massive datasets. We propose two techniques for minimizing disk I/O cost of join operations for both spatial and sequence data. Our techniques o...
Tamer Kahveci, Christian A. Lang, Ambuj K. Singh
GECCO
2007
Springer
163views Optimization» more  GECCO 2007»
13 years 11 months ago
Discovering event evidence amid massive, dynamic datasets
Automated event extraction remains a very difficult challenge requiring information analysts to manually identify key events of interest within massive, dynamic data. Many techniq...
Robert M. Patton, Thomas E. Potok
CLUSTER
2009
IEEE
14 years 3 days ago
Analyzing massive astrophysical datasets: Can Pig/Hadoop or a relational DBMS help?
Abstract— As the datasets used to fuel modern scientific discovery grow increasingly large, they become increasingly difficult to manage using conventional software. Parallel d...
Sarah Loebman, Dylan Nunley, YongChul Kwon, Bill H...
VC
2008
143views more  VC 2008»
13 years 5 months ago
A single-pass GPU ray casting framework for interactive out-of-core rendering of massive volumetric datasets
We present an adaptive out-of-core technique for rendering massive scalar volumes employing single pass GPU raycasting. The method is based on the decomposition of a volumetric dat...
Enrico Gobbetti, Fabio Marton, José Antonio...
DKE
2007
88views more  DKE 2007»
13 years 5 months ago
Regression analysis for massive datasets
In this paper, a two-stage block hypothesis testing following the idea of Fan, Lin and Cheng (2004) is proposed for massive data regression analysis. Variables selection criteria ...
Tsai-Hung Fan, Dennis K. J. Lin, Kuang-Fu Cheng