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Research Practitioner's Handbook on Big Data Analytics PDF

310 Pages·2023·27.861 MB·English
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RESEARCH 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 Apple Academic Press Inc. CRC Press 1265 Goldenrod Circle, NE, 6000 Broken Sound Parkway NW, Palm Bay, FL 32905 USA Suite 300, Boca Raton, FL 33487-2742 USA 760 Laurentian Drive, Unit 19, 4 Park Square, Milton Park, Burlington, ON L7N 0A4, CANADA Abingdon, Oxon, OX14 4RN UK © 2023 by Apple Academic Press, Inc. Apple Academic Press exclusively co-publishes with CRC Press, an imprint of Taylor & Francis Group, LLC Reasonable efforts have been made to publish reliable data and information, but the authors, editors, and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors, editors, and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged, please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. 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

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