1st Edition
Research Practitioner's Handbook on Big Data Analytics
This new volume addresses the growing interest in and use of big data analytics in many industries and in many research fields around the globe; it is a comprehensive resource on the core concepts of big data analytics and the tools, techniques, and methodologies. The book gives the why and the how of big data analytics in an organized and straightforward manner, using both theoretical and practical approaches.
The book’s authors have organized the contents in a systematic manner, starting with an introduction and overview of big data analytics and then delving into pre-processing methods, feature selection methods and algorithms, big data streams, and big data classification. Such terms and methods as swarm intelligence, data mining, the bat algorithm and genetic algorithms, big data streams, and many more are discussed. The authors explain how deep learning and machine learning along with other methods and tools are applied in big data analytics. The last section of the book presents a selection of illustrative case studies that show examples of the use of data analytics in industries such as health care, business, education, and social media.
1. Introduction to Big Data Analytics
Introduction
A Wider Variety of Data
Types and Sources of Big Data
Characteristics of Big Data
Data Property Types
Big Data Analytics
Big Data Analytics Tools with Their Key Features
Techniques of Big Data Analysis
2. Pre-Processing Methods
Data Mining: Need for Preprocessing
Pre-Processing Methods
Challenges of Big Data Streams in Preprocessing
Pre-Processing Methods
3. Feature Selection Methods and Algorithms
Feature Selection Methods
Types of Fs
Swarm Intelligence in Big Data Analytics
Particle Swarm Optimization (PSO)
Bat Algorithm (BA)
Genetic Algorithms
Ant Colony Optimization (ACO)
Artificial Bee Colony Algorithm (ABC)
Cuckoo Search Algorithm
Firefly Algorithm
Grey Wolf Optimization Algorithm (GWO)
Dragonfly Algorithm (DA)
Whale Optimization Algorithm (WOA)
4. Big Data Streams
Introduction
Stream Processing
Benefits of Stream Processing
Streaming Analytics
Real-Time Big Data Processing Lifecycle
Streaming Data Architecture
Modern Streaming Architecture
The Future of Streaming Data in 2021 and Beyond
Big Data and Stream Processing
Framework for Parallelization on Big Data
5. Big Data Classification
Classification in Big Data and Challenges
Machine Learning (ML)
Incremental Learning for Big Data Streams
Ensemble Algorithms
Deep Learning Algorithms
Deep Neural Networks
Categories of Deep Learning Algorithms
6. Case Studies
Introduction
Health Care Analytics: Overview
Big Data Analytics Health Care Systems
Healthcare Companies Implementing Analytics
Social Big Data Analytics
Big Data in Business
Educational Data Analytics
Biography
S. Sasikala, PhD, is Associate Professor and Research Supervisor in the Department of Computer Science, IDE, and Director of Network Operation and Edusat Programs at the University of Madras, Chennai, India. With 23 years of teaching experience, she has held various posts at the university, including Head-in-charge of the Centre for Web-based Learning, Nodal Officer for the UGC Student Redressal Committee, Coordinator for Online Course Development at IDE, and President of the Alumni Association at IDE. Her research interests include imaging, data mining, machine learning, networks, big data, and AI. She has published two books on computer science and published over 27 research articles in leading journals and conference proceedings as well as four book chapters. She has also received best paper awards and women's achievement awards. She is an active reviewer and editorial member for international journals and conferences.
D. Renuka Devi, PhD, is Assistant Professor in the Department of Computer Science, Stella Maris College (Autonomous), Chennai, India. She has 12 years of teaching experience. Her research interests include data mining, machine learning, big data, and AI. She actively participates in continued learning through conferences and professional research. She has published eight research papers and a book chapter in publications from IEEE, Scopus, and Web of Science. She has also presented papers at international conferences and received best paper awards.
Raghvendra Kumar, PhD, is Associate Professor in the Computer Science and Engineering Department at GIET University, India. Dr. Kumar serves as Editor of the book series Internet of Everything: Security and Privacy and the book series Biomedical Engineering: Techniques and Applications (Apple Academic Press). He has published several research papers in international journals and conferences. He has served in many roles for international and national conferences and has authored and edited over 20 computer science books in the field of Internet of Things, data mining, biomedical engineering, big data, robotics, graph theory, and Turing machines.
