A Closer Look at Big Data Analytics

$230.00

Series: Computer Science, Technology and Applications

BISAC: COM018000

Big Data Analytics is a field that dissects, efficiently extricates data from, or in any case manages informational indexes that are excessively huge or complex to be managed by customary information preparing application programming. Information with numerous cases (lines) offers more noteworthy factual force, while information with higher multifaceted nature may prompt a higher bogus disclosure rate. Enormous information challenges incorporate catching information, information stockpiling, information investigation, search, sharing, move, representation, and questioning, refreshing, data security and data source. Large information was initially connected with three key ideas: volume, variety and velocity. Consequently, huge information regularly incorporates information with sizes that surpass the limit of conventional programming to measure inside a satisfactory time and worth.

Current utilization of the term enormous information will in general allude to the utilization of predictive analytics, user behavior analytics, or certain other progressed information investigation techniques that concentrate an incentive from information, and sometimes to a specific size of informational index. There is little uncertainty that the amounts of information now accessible are undoubtedly enormous, however that is not the most important quality of this new information biological system. Investigation of informational indexes can discover new relationships to spot business patterns or models. Researchers, business persons, clinical specialists, promoting and governments consistently meet challenges with huge informational collections in territories including Internet look, fintech, metropolitan informatics, and business informatics. Researchers experience constraints in e-Science work, including meteorology, genomics, connectomics, complex material science reproductions, science and ecological exploration.

The main objective of this book is to write about issues, challenges, opportunities, and solutions in novel research projects about big data in various domains. The topics of interest include, but are not limited to: efficient storage, management and sharing large scale of data; novel approaches for analyzing data using big data technologies; implementation of high performance and/or scalable and/or real-time computation algorithms for analyzing big data; usage of various data sources like historical data, social networking media, machine data and crowd-sourcing data; using machine learning, visual analytics, data mining, spatio-temporal data analysis and statistical inference in different domains (with large scale datasets); Legal and ethical issues and solutions for using, sharing and publishing large datasets; and the results of data analytics, security and privacy issues.

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Table of Contents

Preface

Chapter 1. Artificial Intelligence for Knowing the Anticipation of Client from Online Food Delivery Using Big Data
(H.M.Moyeenudin, R. Anandan – School of Hotel & Catering Management, Vels Institute of Science, Technology and Advanced Studies, (VISTAS) Deemed to be University, Pallavaram, Chennai, India, et al.)

Chapter 2. An Overview on IOT Data Analytics with a Survey on CNN Accelerator Architecture
(S. Karthik and K. Priyadarsini – Department of ECE, Faculty of Engineering and Technology, Vadapalani Campus, SRM IST, Vadapalani, Chennai, India, et al.)

Chapter 3. Big Data in Multi-Decision Making System of Aeronautic Industry
(P. Kalaichelvi, V. AKila, T. P. Rani, S. Sowmiya and C. Divya – Department of Information Technology, Sri Sairam Engineering College, Chennai, Tamil Nadu, India, et al.)

Chapter 4. New Trends and Applications of Big Data Analytics for Medical Science and Healthcare
(T. Nalini and Sudhakar Murugesan – Department of Computer Science and Engineering, Dr. MGR Educational and Research Institute, Maduravoyal, Chennai, Tamil Nadu, India, et al.)

Chapter 5. Deep Neural Networks in Bioinfomatics for MOTIF Identification
(D. Shine Babu and Latha Parthiban – Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Tamilnadu, India, et al.)

Chapter 6. Utilizing Scratch to Creating Computational Thinking at School with Artificial Intelligence
(Matta Krishna Kumari, T. P. Latchoumi, G. Kalusuraman, M. Chithambarathanu and Latha Parthiban – Department of Computer Science and Engineering, VFSTR (Deemed to be University), Guntur, Andhra Pradesh, India, et al.)

Chapter 7. Tracking System for Birds Migration using Sensors
(Battula. Bhavya, T. R. Rajesh, T. P. Latchoumi, Narra. Harika and Latha Parthiban – Department of Computer Science and Engineering, VFSTR (Deemed to be University), Guntur, Andhra Pradesh, India, et al.)

Chapter 8. Big Data Analytics Tools
(Dr. R. Anandan, Syed Rizwan and Usha Kumari – Department of Computer Science and Engineering, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, India)

Chapter 9. Data Mining Techniques with its Applications
(J. Priya and R. Anandan – Research Scholar CSE Department – VISTAS, Chennai, India & MallaReddy Engineering College and Management Sciences, Hyderabad, India, et al.)

Chapter 10. Design of Computationally Intelligent Decision Support System Using Data Analytics
(Maithili Devi Reddy and Latha Parthiban – Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Tamilnadu, India, et al.)

Chapter 11. Data Analytics using Computationally Intelligent Agents for Medical Diagnosis
(Maithili Devi Reddy and Latha Parthiban – Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Tamilnadu, India, et al.)

Chapter 12. Stability and Confidentiality Mechanism in Big Data
(D. Raghunath Kumar Babu, R. Balakrishna and R. Anandan – Department of Computer Science and Engineering, JNTUA College of Engineering, Muddanur Road, Pulivendula, Andhra Pradesh, India, et al.)

Index

Reviews

“R. Anandan, in A Closer Look at Big Data Analytics, offers some perspectives on how big data may be analyzed and perhaps kept safe. It does not read like a manual, but more of a tourist guidebook into the topic. The work mostly argues for the power of big data analytics to inform on behaviors in the world…. Read more at >>>” – Shalin Hai-Jew, Instructional Designer/Researcher, Kansas State University. Published in C2C Digital Magazine (Fall 2021 / Winter 2022).

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