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...
Automated event extraction remains a very difficult challenge requiring information analysts to manually identify key events of interest within massive, dynamic data. Many techniq...
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...
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...
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 ...