With the advent of high-performance COTS clusters, there is a need for a simple, scalable and faulttolerant parallel programming and execution paradigm. In this paper, we show that...
Reza Farivar, Abhishek Verma, Ellick Chan, Roy H. ...
This paper explores the challenge of scaling up language processing algorithms to increasingly large datasets. While cluster computing has been available in commercial environment...
Background: MapReduce is a parallel framework that has been used effectively to design largescale parallel applications for large computing clusters. In this paper, we evaluate th...
PixelMaps are a new pixel-oriented visual data mining technique for large spatial datasets. They combine kerneldensity-based clustering with pixel-oriented displays to emphasize c...
Daniel A. Keim, Christian Panse, Mike Sips, Stephe...
—Mining trajectory data has been gaining significant interest in recent years. However, existing approaches to trajectory clustering are mainly based on density and Euclidean di...