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Big Data Analytics Methods and Applications /

This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover t...

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Corporate Author: SpringerLink (Online service)
Other Authors: Pyne, Saumyadipta. (Editor, http://id.loc.gov/vocabulary/relators/edt), Rao, B.L.S. Prakasa. (Editor, http://id.loc.gov/vocabulary/relators/edt), Rao, S.B. (Editor, http://id.loc.gov/vocabulary/relators/edt)
Language:English
Published: New Delhi : Springer India : Imprint: Springer, 2016.
Edition:1st ed. 2016.
Subjects:
Statistics .
Data mining.
Applied mathematics.
Engineering mathematics.
Statistics and Computing/Statistics Programs.
Statistics for Life Sciences, Medicine, Health Sciences.
Statistics for Social Sciences, Humanities, Law.
Statistics for Business, Management, Economics, Finance, Insurance.
Data Mining and Knowledge Discovery.
Applications of Mathematics.
Online Access:https://doi.org/10.1007/978-81-322-3628-3
Table of Contents:
  • Chapter 1. Introduction: The Promises and Challenges of Big Data Analytics
  • Chapter 2. Massive Data Analysis: Tasks, Tools, Applications and Challenges
  • Chapter 3. Statistical Challenges with Big Data in Management Science
  • Chapter 4. Application of Mixture Models to Large Datasets
  • Chapter 5. An Efficient Partition-Repetition Approach in Clustering of Big Data
  • Chapter 6. Multithreaded Graph Algorithms for Large-scale Analytics
  • Chapter 7. On-line Graph Partitioning with an Affine Message Combining Cost Function
  • Chapter 8. Big Data Analytics Platforms for Real-time Applications in IoT
  • Chapter 9. Complex Event Processing in Big Data Systems
  • Chapter 10. Unwanted Traffic Identification in Large-scale University Networks: A Case Study
  • Chapter 11. Application-Level Benchmarking of Big Data Systems
  • Chapter 12. Managing Large Scale Standardized Electronic Healthcare Records
  • Chapter 13. Microbiome Data Mining for Microbial Interactions and Relationships
  • Chapter 14. A Nonlinear Technique for Analysis of Big Data in Neuroscience
  • Chapter 15. Big Data and Cancer Research.

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