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Big Data Analytics

Overview of attention for book
Cover of 'Big Data Analytics'

Table of Contents

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    Book Overview
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    Chapter 1 Big Data Analytics: Views from Statistical and Computational Perspectives
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    Chapter 2 Massive Data Analysis: Tasks, Tools, Applications, and Challenges
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    Chapter 3 Statistical Challenges with Big Data in Management Science
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    Chapter 4 Application of Mixture Models to Large Datasets
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    Chapter 5 An Efficient Partition-Repetition Approach in Clustering of Big Data
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    Chapter 6 Online Graph Partitioning with an Affine Message Combining Cost Function
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    Chapter 7 Big Data Analytics Platforms for Real-Time Applications in IoT
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    Chapter 8 Complex Event Processing in Big Data Systems
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    Chapter 9 Unwanted Traffic Identification in Large-Scale University Networks: A Case Study
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    Chapter 10 Application-Level Benchmarking of Big Data Systems
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    Chapter 11 Managing Large-Scale Standardized Electronic Health Records
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    Chapter 12 Microbiome Data Mining for Microbial Interactions and Relationships
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    Chapter 13 A Nonlinear Technique for Analysis of Big Data in Neuroscience
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    Chapter 14 Big Data and Cancer Research
Attention for Chapter 10: Application-Level Benchmarking of Big Data Systems
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Citations

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Summary Dimensions citations
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Chapter title
Application-Level Benchmarking of Big Data Systems
Chapter number 10
Book title
Big Data Analytics
Published by
Springer India, October 2016
DOI 10.1007/978-81-322-3628-3_10
Book ISBNs
978-8-13-223626-9, 978-8-13-223628-3
Authors

Chaitanya Baru, Tilmann Rabl

Editors

Saumyadipta Pyne, B.L.S. Prakasa Rao, S.B. Rao

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 44%
Student > Ph. D. Student 2 22%
Student > Master 2 22%
Student > Doctoral Student 1 11%
Readers by discipline Count As %
Computer Science 4 44%
Business, Management and Accounting 2 22%
Economics, Econometrics and Finance 1 11%
Chemistry 1 11%
Engineering 1 11%
Other 0 0%

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