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Big data analyticsmethods and applic...

Pyne, Saumyadipta.

Big data analyticsmethods and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big data analyticsedited by Saumyadipta Pyne, B.L.S. Prakasa Rao, S.B. Rao.
Reminder of title:	methods and applications /
other author:	Pyne, Saumyadipta.
Published:	New Delhi :Springer India :2016.
Description:	xii, 276 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Big data.
Online resource:	http://dx.doi.org/10.1007/978-81-322-3628-3
ISBN:	9788132236283$q(electronic bk.)

Big data analyticsmethods and applications /
Big data analyticsmethods and applications /[electronic resource] :edited by Saumyadipta Pyne, B.L.S. Prakasa Rao, S.B. Rao. - New Delhi :Springer India :2016. - xii, 276 p. :ill., digital ;24 cm.

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.

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 technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.

ISBN: 9788132236283$q(electronic bk.)

Standard No.: 10.1007/978-81-322-3628-3doiSubjects--Topical Terms:

609582
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Big data analyticsmethods and applications /
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505 0 $a 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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