Referential DNA Data Compression using Hadoop Map Reduce Framework

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 ...

Handling Data Skew in Map Reduce Using Hadoop Libra

There are many efficient tools significantly uses Map Reduce applications that assigns data with their corresponding tasks in parallel and distributed data processing. LIBRA symbolizes the lightweight problems of data skew with input data applications that can overlap map and reduce strategies. This is one of the innovative and accurate distribution methods for intermediate data sampling with n...

Medical Application of Privacy Preservation by Big Data Analysis Using Hadoop Map Reduce Framework

The Map Reduce framework has become the de-facto framework for large-scale data analysis and data mining. The computer industry is being challenged to develop methods and techniques for affordable data processing on large datasets at optimum response times. The technical challenges in dealing with the increasing demand to handle vast quantities of data is daunting and on the rise. One of the re...

Analysing Distributed Big Data through Hadoop Map Reduce

This term paper focuses on how the big data is analysed in a distributed environment through Hadoop Map Reduce. Big Data is same as “small data” but bigger in size. Thus, it is approached in different ways. Storage of Big Data requires analysing the characteristics of data. It can be processed by the employment of Hadoop Map Reduce. Map Reduce is a programming model working parallel for large c...

Performance Enhancement of Big Data Processing in Hadoop Map/reduce