Analysis for guaranteeing performance in map reduce systems with hadoop and R

Performance Enhancement of Big Data Processing in Hadoop Map/reduce

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...

Improving Performance of Heterogeneous Hadoop Clusters using Map Reduce for Big Data

The key problem that arises due to enormous growth of connectivity between devices and systems is creating so much data at an exponential rate that a feasible solution for processing it is becoming difficult day by day. Therefore, developing a platform for such advanced level of data processing, hardware as well as software enhancements need to be conducted to come in level with such magnanimou...

Implementation and Analysis of Join Algorithms to handle skew for the Hadoop Map/Reduce Framework

he Map/Reduce framework-a parallel processing paradigm-is widely being used for large scale distributed data processing. Map/Reduce can perform typical relational database operations like selection, aggregation, and projection etc. However, binary relational operators like join, cartesian product, and set operations are difficult to implement with Map/Reduce. Map/Reduce can process homogeneous ...