Table Of ContentRESEARCH PRACTITIONER’S
HANDBOOK ON BIG DATA ANALYTICS
RESEARCH PRACTITIONER’S
HANDBOOK ON BIG DATA ANALYTICS
S. Sasikala, PhD
Renuka Devi D, PhD
Raghvendra Kumar, PhD
Editor
First edition published 2023
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Library and Archives Canada Cataloguing in Publication
Title: Research practitioner's handbook on big data analytics / S. Sasikala, PhD, D. Renuka Devi, Raghvendra Kumar, PhD.
Names: Sasikala, S., 1970- author. | Devi, D. Renuka, 1980- author. | Kumar, Raghvendra, 1987- author.
Description: First edition. | Includes bibliographical references and index.
Identifiers: Canadiana (print) 20220437475 | Canadiana (ebook) 20220437491 | ISBN 9781774910528 (hardcover) |
ISBN 9781774910535 (softcover) | ISBN 9781003284543 (ebook)
Subjects: LCSH: Big data—Handbooks, manuals, etc. | LCSH: Big data—Research—Handbooks, manuals, etc. |
LCSH: Data mining—Handbooks, manuals, etc. | LCSH: Electronic data processing—Handbooks, manuals, etc. |
LCGFT: Handbooks and manuals.
Classification: LCC QA76.9.B45 S27 2023 | DDC 005.7—dc23
Library of Congress Cataloging-in-Publication Data
CIP data on file with US Library of C o ngress
ISBN: 978-1-77491-052-8 (hbk)
ISBN: 978-1-77491-053-5 (pbk)
ISBN: 978-1-00328-454-3 (ebk)
About the Authors
S. Sasikala, PhD
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 Associa-
tion 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 enthu-
siastic 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 edito-
rial member for international journals and conferences. She has been
invited for talks on various emerging topics and chaired sessions in
international conferences.
Renuka Devi D, PhD
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
vi About the Authors
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.
About the Editor
Raghvendra Kumar, PhD
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.
Contents
Abbreviations .....................................................................................................xiii
Preface .................................................................................................................xv
Introduction ....................................................................................................... xvii
1. Introduction to Big Data Analytics............................................................. 1
Abstract................................................................................................................................ 1
1.1 Introduction .................................................................................................................1
1.2 Wider Variety of Data ................................................................................................. 3
1.3 Types and Sources of Big Data ................................................................................... 4
1.4 Characteristics of Big Data ......................................................................................... 9
1.5 Data Property Types ..................................................................................................16
1.6 Big Data Analytics ....................................................................................................18
1.7 Big Data Analytics Tools with Their Key Features ..................................................25
1.8 Techniques of Big Data Analysis ..............................................................................32
Keywords ...........................................................................................................................42
References .........................................................................................................................42
2. Preprocessing Methods .............................................................................. 45
Abstract..............................................................................................................................45
2.1 Data Mining—Need of Preprocessing ......................................................................45
2.2 Preprocessing Methods .............................................................................................49
2.3 Challenges of Big Data Streams in Preprocessing ....................................................59
2.4 Preprocessing Methods .............................................................................................60
Keywords ...........................................................................................................................68
References .........................................................................................................................68
3. Feature Selection Methods and Algorithms ............................................ 71
Abstract..............................................................................................................................71
3.1 Feature Selection Methods ........................................................................................71
3.2 Types of Fs ................................................................................................................72
3.3 Online Fs Methods ....................................................................................................78
3.4 Swarm Intelligence in Big Data Analytics ................................................................79
3.5 Particle Swarm Optimization ....................................................................................86
3.6 Bat Algorithm ............................................................................................................86
