Handbook of Research for Big Data: Concepts and Techniques

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. Cover Half Title Title Page Copyright Page About the Editors Table of Contents Contributors Abbreviations Preface 1. Big Data in Governance in India: Case Studies 2. Design and Analysis of Reconfigurable Computing of SoC for IoT Applications 3. A Review of Different Data Mining Techniques Used in Big Data Applications 4. Big Data Applications in Transportation Systems Using the Internet of Things 5. Overview of Big Data and Natural Language Processing: A Powerful Combination for Research 6. An Insight into Big Data and Its Pertinence 7. Big Data Science: Models and Approaches, Characteristics, Challenges, and Applications 8. Conceptual Frameworks for Big Data Visualization: Discussion on Models, Methods, and Artificial Intelligence for Graphical Representations of Data 9. Role of Machine Learning in Big Data Peregrination 10. Artificial Neural Networks: Fundamentals, Design, and Applications 11. Big Data: Trends, Challenges, Opportunities, Tools, Success Factors, and the Way Toward Pandemic Analytics 12. Applying Tenacious Machine Learning Classification Techniques for Drug-Free Nipah Virus Prediction 13. An Approach for Controlling Disaster Management by Machine Learning Technique Index