Data Intensive Computing Applications for Big Data
eBook - PDF

Data Intensive Computing Applications for Big Data

  1. 618 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Data Intensive Computing Applications for Big Data

About this book

The book 'Data Intensive Computing Applications for Big Data' discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing paradigms. It brings together researchers to report their latest results or progress in the development of the above mentioned areas. Since there are few books on this specific subject, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings. The book is intended as a reference work for advanced undergraduates and graduate students, as well as multidisciplinary, interdisciplinary and transdisciplinary research workers and scientists on the subjects of big data and cloud/parallel and distributed computing, and explains didactically many of the core concepts of these approaches for practical applications. It is organized into 24 chapters providing a comprehensive overview of big data analysis using parallel computing and addresses the complete data science workflow in the cloud, as well as dealing with privacy issues and the challenges faced in a data-intensive cloud computing environment. The book explores both fundamental and high-level concepts, and will serve as a manual for those in the industry, while also helping beginners to understand the basic and advanced aspects of big data and cloud computing.

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Yes, you can access Data Intensive Computing Applications for Big Data by M. Mittal,V.E. Balas,D.J. Hemanth,Mamta Mittal,Valentina Emilia Balas,D. Jude Hemanth,Raghvendra Kumar in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Science General. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Title Page
  2. About the Book
  3. About the Editors
  4. Preface
  5. Contents
  6. A Survey of Diversified Domain of Big Data Technologies
  7. Big Data Technologies
  8. Steps for Implementing Big Data and Its Security Challenges
  9. Big Data Security Solutions in Cloud
  10. Big Data Analysis in Cloud Using Machine Learning
  11. Big Data Analysis Using Machine Learning Approach to Compute Data
  12. Data Intensive Computing Application for Big Data
  13. Uncertainty Detection in Unstructured Big Data
  14. Parallel Computing: A Paradigm to Unimaginable Computing Speed and Efficiency
  15. Application of Big Data Analytics in Cloud Computing via Machine Learning
  16. A Novel Mechanism for Cloud Data Management in Distributed Environment
  17. Spark SQL with Hive Context or SQL Context
  18. Renewing Computing Paradigms for More Efficient Parallelization of Single-Threads
  19. MongoDB as an Efficient Graph Database: An Application of Document Oriented NOSQL Database
  20. Big Data Analytics for Prevention and Control of HIV/AIDS
  21. Performance Analysis of Deadlock Prevention and MUTEX Detection Algorithms in Distributed Environment
  22. Real Time Location Tracking Map Matching Approaches for Road Navigation Applications
  23. Accurate Prediction of Life Style Based Disorders by Smart Healthcare Using Machine Learning and Prescriptive Big Data Analytics
  24. Parallel Computing Contrive Optimized NFB Through QEEG & LENS Approach
  25. S-ARRAY: Highly Scalable Parallel Sorting Algorithm
  26. Protein Synthesis Based Discretization Method for Knowledge Discovery
  27. Scala Programming for Big-Data Application
  28. Fading Channel and Imperfect Channel Estimation for OFDM in Wireless Communication
  29. Blockchain Innovation and Its Impact on Business Banking Operations
  30. Subject Index
  31. Author Index