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03859nam a22005175i 4500 |
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978-81-322-3628-3 |
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|a 9788132236283
|9 978-81-322-3628-3
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|2 doi
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|a 519.5
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|a Big Data Analytics
|h [electronic resource] :
|b Methods and Applications /
|c edited by Saumyadipta Pyne, B.L.S. Prakasa Rao, S.B. Rao.
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|a New Delhi :
|b Springer India :
|b Imprint: Springer,
|c 2016.
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| 300 |
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|a XII, 276 p. 67 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a text file
|b PDF
|2 rda
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|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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|a 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.
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| 650 |
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|a Statistics.
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| 650 |
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|a Data mining.
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| 650 |
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|a Applied mathematics.
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| 650 |
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|a Engineering mathematics.
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|a Statistics.
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|a Statistics and Computing/Statistics Programs.
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| 650 |
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|a Statistics for Life Sciences, Medicine, Health Sciences.
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| 650 |
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|a Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
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| 650 |
2 |
4 |
|a Statistics for Business/Economics/Mathematical Finance/Insurance.
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| 650 |
2 |
4 |
|a Data Mining and Knowledge Discovery.
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| 650 |
2 |
4 |
|a Applications of Mathematics.
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| 700 |
1 |
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|a Pyne, Saumyadipta.
|e editor.
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| 700 |
1 |
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|a Rao, B.L.S. Prakasa.
|e editor.
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| 700 |
1 |
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|a Rao, S.B.
|e editor.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9788132236269
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| 856 |
4 |
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|u http://dx.doi.org/10.1007/978-81-322-3628-3
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-SMA
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| 950 |
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|a Mathematics and Statistics (Springer-11649)
|
