Research Practitioner's Handbook on Big Data Analytics
- Available for pre-order on April 14, 2023. Item will ship after May 5, 2023
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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.
Table of Contents
1. Introduction to Big Data Analytics 2. Pre-Processing Methods 3. Feature Selection Methods and Algorithms 4. Big Data Streams 5. Big Data Classification 6. Case Studies
S. Sasikala, PhD, is Associate Professor and Research Supervisor in the Department of Computer Science and Head-in-Charge of the Centre for Web-based Learning, IDE, at the University of Madras, Chennai, India. She has 25 years of teaching experience and has coordinated computer-related courses. She has been an active chair for various board of studies meetings held at the various institutions and as acted as an advisor for research. She has participated in administrative and research activities such as guiding research scholars, writing and editing textbooks, and publishing articles in many reputed journals. Her research interests include image mining, data mining, machine learning, networks, big data, and AI. She has published two books on computer science topics, several book chapters, and over 27 research articles in leading journals and conference proceedings with 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 of international journals and conferences. She has been invited for talks on various emerging topics and chaired sessions in international conferences.
D. Renuka Devi is Assistant Professor in the Department of Computer Science, Stella Maris College (Autonomous), Chennai, India. She has over 11 years of teaching experience. Her research interests include data mining, machine learning, big data, and AI. Ms. Devi is an active researcher and academician. She has published seven research papers and one book chapter to date with IEEE, Scopus, and Web of Science. She has also presented papers at international conferences and has received a best paper award.
Raghvendra Kumar, PhD, is an 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.