Handbook of Research for Big Data : Concepts and Techniques book cover
1st Edition

Handbook of Research for Big Data
Concepts and Techniques

  • Available for pre-order. Item will ship after September 30, 2021
ISBN 9781771889803
September 30, 2021 Forthcoming by Apple Academic Press
375 Pages

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Book Description

Data has become a valuable asset like never before. Today the challenge is not a shortage of data but the need for techniques and methods capable enough to be able to glean valuable insights from the fast-flowing mass of Big Data. This new volume, Handbook of Research for Big Data: Concepts and Techniques, helps to meet the challenge of managing and using Big Data by presenting new research on various technological advances in the field.

The chapters in the book present information on important applications, concepts, and technologies for Big Data in the present industry and market scenario. It looks at research domain issues and their solutions as well as various research case studies, research plans, methodologies, and related data sets for the four Vs: volume, velocity, variety, and veracity.

Chapters discuss Big Data in governance, transportation, disaster management, epidemiology, and more. The book covers design and analysis of reconfigurable computing of SoC for IoT, data mining techniques and applications, the use of natural language processing in big data, and more.

The volume is a valuable resource for researchers from both academia and industry to learn about and enhance their knowledge and skills in the broad area of big data computing and applications.

Table of Contents

1. Big Data in Governance in India: Case Studies
Mukesh Choubisa

2. Design and Analysis of Reconfigurable Computing of SoC for IoT Applications
Ipseeta Nanda and Nibedita Adikari

3. A Review of Different Data Mining Techniques Used in Big Data Applications
Chandrakanta Mahanty, Devpriya Panda, and Brojo Kishore Mishra

4. Big Data Applications in Transportation Systems Using the Internet of Things
Sunil Kumar Gautam, Riddhi B. Prajapati, and Hari Om

5. Overview of Big Data and Natural Language Processing: A Powerful Combination for Research
Bishwa Ranjan Das and Brojokishore Mishra

6. An Insight to Big Data and Its Pertinence
Lopamudra Hota and Prasant Kumar Dash

7. Big Data Science: Models and Approaches, Characteristics, Challenges, and Applications
Riyanshi Gupta, Kartik Krishna Bhardwaj, and Deepak Kumar Sharma

8. Conceptual Frameworks for Big Data Visualization: Discussion on Models, Methods, and Artificial Intelligence for Graphical Representations of Data
Cherilyn Conner, Jim Samuel, Myles Garvey, Yana Samuel, and Andrey Kretinin

9. Machine Learning Bumps into Big Data Peregrination
Aradhana Behura and Sanjaya Kumar Panda

10. Artificial Neural Networks: Fundamentals, Design, and Applications
Nishant Kashyap, Anjana Mishra, and Brojo Kishore Mishra

11. Big Data: Trends, Challenges, Opportunities, Tools, Success Factors, and the Way toward Pandemic Analytics
P. R. Anisha, C. Kishor Kumar Reddy, and Nguyen Gia Nhu

12. Applying Tenacious Machine Learning Classification Techniques for Drug-Free Nipah Virus Prediction
M. Kannan and C. Priya

13. An Approach for Controlling Disaster Management by Machine Learning Technique
Subhashree Sahoo, Debabrata Dansana, and Brojo Kishore Mishra

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Brojo Kishore Mishra, PhD, is a Professor in the Computer Science and Engineering Department at the Gandhi Institute of Engineering and Technology University (GIET), Gunupur, Odisha, India. He has published more than 30 research papers in national and international conference proceedings, over 25 research papers in peer-reviewed journals, and over 20 book chapters, and has authored two books and edited three books to date. His research interests include data mining and big data analysis, machine learning, soft computing, and evolutionary computation. He received his PhD degree in Computer Science from the Berhampur University, Brahmapur, Odisha, India.

Vivek Kumar, PhD, is a researcher in the NLP field. He formerly worked as a Research Engineer for a SHiP (search for hidden particles) project of CERN extended with NUST-MISIS. He has also rendered his services to the Embassy of India in Moscow to the education and defense sector for strengthening Indo-Russian bilateral relations. He is a member of the Defense and Security Software Engineers Association, Italy. He has authored several publications and is also reviewer, editor, and TP member of several conferences and journals of IEEE, ACM, Springer, Elsevier, MDPI, and IGI-Global. His research interests include machine learning, deep learning, natural language processing and sentiment analysis applied in the healthcare domain. He received his MS degree from NUST-MiSIS, Russian Federation in 2007.

Sanjaya Kumar Panda, PhD, is working as an Assistant Professor and Head of the Department of Computer Science & Engineering at the Indian Institute of Information Technology, Design and Manufacturing, Kurnool, Andhra Pradesh, India. He formerly worked as an Assistant Professor in the Department of IT at VSSUT, Burla, Odisha, India. He received a PhD degree from IIT (ISM) Dhanbad, Jharkhand, India; his MTech degree from NIT, Rourkela, Odisha, India; and a BTech degree from VSSUT, Burla, Odisha, India in CSE. He received two silver medal awards for best graduate and best postgraduate in CSE. Other awards include an institution award, IEEE brand ambassador designation, and SGSITS national award for the best research work by a young teacher of engineering college for the year 2017. He was also a faculty with maximum publishing in CSI publications award, young IT professional award (2017 and 2016), young scientist award, CSI paper presenter award at an international conference, and CSI distinguished speaker award. He has published more than 60 papers in reputed journals and conferences. He is a member of IEEE, an associate member of IEI, life member of ISTE, and a life member of CSI, IAENG, IACSIT, UACEE, ACEEE, and SDIWC. His current research interests include recommender systems, cloud computing, big data analytics, grid computing, fault tolerance, and load balancing. He has delivered several invited talks and has chaired sessions at many national and international conferences and workshops. He acted as a reviewer for many reputed journals. He also acted as guest editor in many international journals.

Prayag Tiwari, PhD, is currently a Marie Sklodowska Curie Researcher with the University of Padua, Italy. He was previously a Research Assistant with NUST MISIS, and he has had teaching and industrial work experience. He has several publications in journals, book series, and conferences of IEEE, ACM, Springer, Elsevier, MDPI, Taylor and Francis, and IGI-Global. His research interests include machine learning, deep learning, quantum inspired machine learning, and information retrieval. He received his MSc degree from NUST MISIS, Moscow. He is currently pursuing a PhD degree with the University of Padova, Italy.