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Resource Management for Big Data Platforms

Overview of attention for book
Cover of 'Resource Management for Big Data Platforms'

Table of Contents

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    Book Overview
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    Chapter 1 Performance Modeling of Big Data-Oriented Architectures
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    Chapter 2 Workflow Scheduling Techniques for Big Data Platforms
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    Chapter 3 Cloud Technologies: A New Level for Big Data Mining
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    Chapter 4 Agent-Based High-Level Interaction Patterns for Modeling Individual and Collective Optimizations Problems
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    Chapter 5 Maximize Profit for Big Data Processing in Distributed Datacenters
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    Chapter 6 Energy and Power Efficiency in Cloud
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    Chapter 7 Context-Aware and Reinforcement Learning-Based Load Balancing System for Green Clouds
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    Chapter 8 High-Performance Storage Support for Scientific Big Data Applications on the Cloud
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    Chapter 9 Information Fusion for Improving Decision-Making in Big Data Applications
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    Chapter 10 Load Balancing and Fault Tolerance Mechanisms for Scalable and Reliable Big Data Analytics
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    Chapter 11 Fault Tolerance in MapReduce: A Survey
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    Chapter 12 Big Data Security
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    Chapter 13 Big Biological Data Management
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    Chapter 14 Optimal Worksharing of DNA Sequence Analysis on Accelerated Platforms
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    Chapter 15 Feature Dimensionality Reduction for Mammographic Report Classification
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    Chapter 16 Parallel Algorithms for Multirelational Data Mining: Application to Life Science Problems
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    Chapter 17 Parallelization of Sparse Matrix Kernels for Big Data Applications
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    Chapter 18 Delivering Social Multimedia Content with Scalability
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    Chapter 19 A Java-Based Distributed Approach for Generating Large-Scale Social Network Graphs
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    Chapter 20 Predicting Video Virality on Twitter
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    Chapter 21 Big Data Uses in Crowd Based Systems
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    Chapter 22 Evaluation of a Web Crowd-Sensing IoT Ecosystem Providing Big Data Analysis
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    Chapter 23 A Smart City Fighting Pollution, by Efficiently Managing and Processing Big Data from Sensor Networks
Overall attention for this book and its chapters
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Mentioned by

2 tweeters


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49 Mendeley
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Resource Management for Big Data Platforms
Published by
Springer International Publishing, January 2016
DOI 10.1007/978-3-319-44881-7
978-3-31-944880-0, 978-3-31-944881-7

Florin Pop, Joanna Kołodziej, Beniamino Di Martino

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 14%
Researcher 5 10%
Student > Master 4 8%
Student > Bachelor 2 4%
Professor 1 2%
Other 1 2%
Unknown 29 59%
Readers by discipline Count As %
Computer Science 12 24%
Engineering 4 8%
Social Sciences 2 4%
Unknown 31 63%