Big data distributed processing system using RHadoop

A Survey on Geographically Distributed Big-Data Processing using MapReduce

Hadoop and Spark are widely used distributed processing frameworks for large-scale data processing in an efficient and fault-tolerant manner on private or public clouds. These big-data processing systems are extensively used by many industries, e.g., Google, Facebook, and Amazon, for solving a large class of problems, e.g., search, clustering, log analysis, different types of join operations, m...

Distributed Processing for Big Data Video Analytics

Intelligent Distributed Processing Methods for Big Data

Motivation Today, “Big Data” is a new information overloading problem in many different areas. Such areas include health cares (e.g., medical records, bioinformatics), e-sciences (e.g., physics, chemistry, and geology), and social sciences (e.g., politics). Thus, as we have various types of feasible data from a number of available sources, it is becoming increasingly more difficult to efficient...

Big Data Analytics: An Approach using Hadoop Distributed File System

Today’s world is driven by Growth and Innovation for a better future. All of which are based on analysis and harnessing of tons of data, typically known as Big Data. The tasks involved for achieving results at such a scale can be challenging and painfully slow. This paper works towards an approach for effectively solving a large and computationally intensive problem by leveraging the capabiliti...

Distributed Data Processing Frameworks for Big Graph Data

Recently we create so much data (2.5 quintillion bytes every day) that 90% of the data in the world today has been created in the last two years alone [1]. This data comes from sensors used to gather traffic or climate information, posts to social media sites, photos, videos, emails, purchase transaction records, call logs of cellular networks, etc. This data is big data. In this report, we fir...