Data Intensive Computing Applications for Big Data

The book Data Intensive Computing Applications for Big Data discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing paradigms. It brings together researchers to report their latest results or progress in the development of the above mentioned areas. Since there are few books on this specific subject, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings. The book is intended as a reference work for advanced undergraduates and graduate students, as well as multidisciplinary, interdisciplinary and transdisciplinary research workers and scientists on the subjects of big data and cloud/parallel and distributed computing, and explains didactically many of the core concepts of these approaches for practical applications.

It is organized into 24 chapters providing a comprehensive overview of big data analysis using parallel computing and addresses the complete data science workflow in the cloud, as well as dealing with privacy issues and the challenges faced in a data-intensive cloud computing environment.

The book explores both fundamental and high-level concepts, and will serve as a manual for those in the industry, while also helping beginners to understand the basic and advanced aspects of big data and cloud computing.

Table of Contents

A Survey of Diversified Domain of Big Data Technologies
Big Data Technologies
Steps for Implementing Big Data and Its Security Challenges
Big Data Security Solutions in Cloud
Big Data Analysis in Cloud Using Machine Learning
Big Data Analysis Using Machine Learning Approach to Compute Data
Data Intensive Computing Application for Big Data
Uncertainty Detection in Unstructured Big Data
Parallel Computing: A Paradigm to Unimaginable Computing Speed and Efficiency
Application of Big Data Analytics in Cloud Computing via Machine Learning
A Novel Mechanism for Cloud Data Management in Distributed Environment
Spark SQL with Hive Context or SQL Context
Renewing Computing Paradigms for More Efficient Parallelization of Single-Threads
MongoDB as an Efficient Graph Database: An Application of Document Oriented NOSQL Database
Big Data Analytics for Prevention and Control of HIV/AIDS
Performance Analysis of Deadlock Prevention and MUTEX Detection Algorithms in Distributed Environment
Real Time Location Tracking Map Matching Approaches for Road Navigation Applications
Accurate Prediction of Life Style Based Disorders by Smart Healthcare Using Machine Learning and Prescriptive Big Data Analytics
Parallel Computing Contrive Optimized NFB Through QEEG & LENS Approach
S-ARRAY: Highly Scalable Parallel Sorting Algorithm
Protein Synthesis Based Discretization Method for Knowledge Discovery
Scala Programming for Big-Data Application
Fading Channel and Imperfect Channel Estimation for OFDM in Wireless Communication
Blockchain Innovation and Its Impact on Business Banking Operations