A Bigdata processing with Hadoop Map Reduce in Cloud Systems

Big Data Processing with Hadoop Map-reduce

The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data ...

Cloud Hadoop Map Reduce For Remote Sensing Image Analysis

Image processing algorithms related to remote sensing have been tested and utilized on the Hadoop MapReduce parallel platform by using an experimental 112-core high-performance cloud computing system that is situated in the Environmental Studies Center at the University of Qatar. Although there has been considerable research utilizing the Hadoop platform for image processing rather than for its...

Performance Enhancement of Big Data Processing in Hadoop Map/reduce

Big Data Processing with Hadoop-MapReduce in Cloud Systems

Received Oct 10 th , 2012 Accepted Oct 31 th , 2012 Today, we‟re surrounded by data like oxygen. The exponential growth of data first presented challenges to cutting-edge businesses such as Google, Yahoo, Amazon, Microsoft, Facebook, Twitter etc. Data volumes to be processed by cloud applications are growing much faster than computing power. This growth demands new strategies for processing and...

Enhancing Map-Reduce Framework for Bigdata with Hierarchical Clustering

MapReduce is a software framework that allows certain kinds of parallelizable or distributable problems involving large data sets to be solved using computing clusters. This paper introduces our experience of grouping internet users by mining a huge volume of web access log of up to 500 gigabytes. The application is realized using hierarchical clustering algorithms with Map-Reduce, a parallel p...