Electronics and Communications Technology

Research Practitioner’s Handbook on Big Data Analytics S. Sasikala, PhD D. Renuka Devi Editor: Raghvendra Kumar, PhD Ordering Info/Buy Book

Published. Available now. Pub Date: May 2023 Hardback Price: see ordering info Hard ISBN: 9781774910528 E-Book ISBN: 9781003284543 Pages: 310pp w/index Binding Type: hardback / ebook Notes: 10 color and 136 b/w illustrations

With the growing interest in and use of big data analytics in many industries and in many research fields around the globe, this new volume addresses the need for a comprehensive resource on the core concepts of big data analytics along with 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.

Research Practitioner’s Handbook on Big Data Analytics will be a valuable addition to the libraries of practitioners in data collection in many industries along with research scholars and faculty in the domain of big data analytics. The book can also serve as a handy textbook for courses in data collection, data mining, and big data analytics.

CONTENTS:

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

Index

About the Authors / Editors:
S. Sasikala, PhD
Associate Professor and Research Supervisor, Department of Computer Science; Head-in-Charge, Centre for Web-based Learning, IDE, University of Madras, Chennai, India

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. She has 23 years of teaching experience and has coordinated computer-related courses with dedication and sincerity. She has acted as Head-in-charge of the Centre for Web-based Learning for three years, beginning in 2019. She holds various posts at the university, including Nodal Officer for the UGC Student Redressal Committee, Coordinator for Online Course Development at IDE, President for Alumni Association at IDE. She has been an active chair in various Board of Studies meetings held at the institution and has acted as an advisor for research. She has participated in administrative activities and shows her enthusiastic participation in research activities by guiding research scholars, writing and editing textbooks, and publishing articles in many reputed journals consistently. Her research interests include image, data mining, machine learning, networks, big data, and AI. She has published two books in the domain of computer science and published over 27 research articles in leading journals and conference proceedings as well as four book chapters, including in publications from IEEE, Scopus, Elsevier, Springer, and Web of Science. 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. She has been invited for talks on various emerging topics and chaired sessions in international conferences.

D. Renuka Devi
Assistant Professor, Department of Computer Science, Stella Maris College (Autonomous), Chennai, India

Renuka Devi D, 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.

Editor: Raghvendra Kumar, PhD
Associate Professor, Computer Science & Engineering Department, GIET University, India

Raghvendra Kumar, PhD, is Associate Professor in the Computer Science and Engineering Department at GIET University, India. He was formerly associated with the Lakshmi Narain College of Technology, Jabalpur, Madhya Pradesh, India. He also serves as Director of the IT and Data Science Department at the Vietnam Center of Research in Economics, Management, Environment, Hanoi, Viet Nam. Dr. Kumar serves as Editor of the book series Internet of Everything: Security and Privacy Paradigm (CRC Press/Taylor & Francis Group) and the book series Biomedical Engineering: Techniques and Applications (Apple Academic Press). He has published a number of research papers in international journals and conferences. He has served in many roles for international and national conferences, including organizing chair, volume editor, volume editor, keynote speaker, session chair or co-chair, publicity chair, publication chair, advisory board member, and technical program committee member. He has also served as a guest editor for many special issues of reputed journals. He authored and edited over 20 computer science books in field of Internet of Things, data mining, biomedical engineering, big data, robotics, graph theory, and Turing machines. He is the Managing Editor of the International Journal of Machine Learning and Networked Collaborative Engineering. He received a best paper award at the IEEE Conference 2013 and Young Achiever Award–2016 by the IEAE Association for his research work in the field of distributed database. His research areas are computer networks, data mining, cloud computing and secure multiparty computations, theory of computer science and design of algorithms.

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