Techniques and Environments for Big Data Analysis: Parallel, Cloud, and Grid Computing

✍ Scribed by Bhabani Shankar Prasad Mishra, Satchidananda Dehuri, Euiwhan Kim, Gi-Name Wang (eds.)

• Publisher: Springer International Publishing
• Year: 2016
• Tongue: English
• Leaves: 199
• Series: Studies in Big Data 17
• Edition: 1
• Category: Library

✦ Synopsis

This volume is aiming at a wide range of readers and researchers in the area of Big Data by presenting the recent advances in the fields of Big Data Analysis, as well as the techniques and tools used to analyze it. The book includes 10 distinct chapters providing a concise introduction to Big Data Analysis and recent Techniques and Environments for Big Data Analysis. It gives insight into how the expensive fitness evaluation of evolutionary learning can play a vital role in big data analysis by adopting Parallel, Grid, and Cloud computing environments.

✦ Table of Contents

Front Matter....Pages i-xi
Introduction to Big Data Analysis....Pages 1-20
Parallel Environments....Pages 21-30
A Deep Dive into the Hadoop World to Explore Its Various Performances....Pages 31-51
Natural Language Processing and Machine Learning for Big Data....Pages 53-74
Big Data and Cyber Foraging: Future Scope and Challenges....Pages 75-100
Parallel GA in Big Data Analysis....Pages 101-111
Evolutionary Algorithm Based Techniques to Handle Big Data....Pages 113-158
Statistical and Evolutionary Feature Selection Techniques Parallelized Using MapReduce Programming Model....Pages 159-180
The Role of Grid Technologies: A Next Level Combat with Big Data....Pages 181-191

✦ Subjects

Computational Intelligence; Data Mining and Knowledge Discovery; Artificial Intelligence (incl. Robotics)