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

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.