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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Další autoři: Pyne, Saumyadipta (Editor)
Rao, B. L. S. Prakasa (Editor)
Rao, S. B. (Editor)
Korporace: ProQuest Ebook Central (online služba) (Distributor) 
Médium: E-kniha
Jazyk:angličtina
Vydáno: New Delhi : Springer, 2016
Žánr/forma:sborníky
elektronické knihy
ISBN:978-81-322-3628-3
978-81-322-3626-9
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On-line přístup:Plný text
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505 0 |a Big Data Analytics: Views from Statistical and Computational Perspectives -- Massive Data Analysis: Tasks, Tools, Applications, and Challenges -- Statistical Challenges with Big Data in Management Science -- Application of Mixture Models to Large Datasets -- An Efficient Partition-Repetition Approach in Clustering of Big Data -- Online Graph Partitioning with an Affine Message Combining Cost Function -- Big Data Analytics Platforms for Real-Time Applications in IoT -- Complex Event Processing in Big Data Systems -- Unwanted Traffic Identification in Large-Scale University Networks: A Case Study -- Application-Level Benchmarking of Big Data Systems -- Managing Large-Scale Standardized Electronic Health Records -- Microbiome Data Mining for Microbial Interactions and Relationships -- A Nonlinear Technique for Analysis of Big Data in Neuroscience -- Big Data and Cancer Research 
520 |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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