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Big data optimization = recent devel...

Emrouznejad, Ali.

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Big data optimization = recent developments and challenges /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big data optimization/ edited by Ali Emrouznejad.
Reminder of title:	recent developments and challenges /
other author:	Emrouznejad, Ali.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xv, 487 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Big data: Who, What and Where? Social, Cognitive and Journals Map of Big Data Publications with Focus on Optimization -- Setting up a Big Data Project: Challenges, Opportunities, Technologies and Optimization -- Optimizing Intelligent Reduction Techniques for Big Data -- Performance Tools for Big Data Optimization -- Optimising Big Images -- Interlinking Big Data to Web of Data -- Topology, Big Data and Optimization -- Applications of Big Data Analytics Tools for Data Management -- Optimizing Access Policies for Big Data Repositories: Latency Variables and the Genome Commons -- Big Data Optimization via Next Generation Data Center Architecture -- Big Data Optimization within Real World Monitoring Constraints -- Smart Sampling and Optimal Dimensionality Reduction of Big Data Using Compressed Sensing -- Optimized Management of BIG Data Produced in Brain Disorder Rehabilitation -- Big Data Optimization in Maritime Logistics -- Big Network Analytics Based on Nonconvex Optimization -- Large-scale and Big Optimization Based on Hadoop -- Computational Approaches in Large-Scale Unconstrained Optimization -- Numerical Methods for Large-Scale Nonsmooth Optimization -- Metaheuristics for Continuous Optimization of High-Dimensional Problems: State of the Art and Perspectives -- Convergent Parallel Algorithms for Big Data Optimization Problems.
Contained By:	Springer eBooks
Subject:	Big data. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-30265-2
ISBN:	9783319302652

Big data optimization = recent developments and challenges /
Big data optimizationrecent developments and challenges /[electronic resource] :edited by Ali Emrouznejad. - Cham :Springer International Publishing :2016. - xv, 487 p. :ill. (some col.), digital ;24 cm. - Studies in big data,v.182197-6503 ;. - Studies in big data ;v.1..

Big data: Who, What and Where? Social, Cognitive and Journals Map of Big Data Publications with Focus on Optimization -- Setting up a Big Data Project: Challenges, Opportunities, Technologies and Optimization -- Optimizing Intelligent Reduction Techniques for Big Data -- Performance Tools for Big Data Optimization -- Optimising Big Images -- Interlinking Big Data to Web of Data -- Topology, Big Data and Optimization -- Applications of Big Data Analytics Tools for Data Management -- Optimizing Access Policies for Big Data Repositories: Latency Variables and the Genome Commons -- Big Data Optimization via Next Generation Data Center Architecture -- Big Data Optimization within Real World Monitoring Constraints -- Smart Sampling and Optimal Dimensionality Reduction of Big Data Using Compressed Sensing -- Optimized Management of BIG Data Produced in Brain Disorder Rehabilitation -- Big Data Optimization in Maritime Logistics -- Big Network Analytics Based on Nonconvex Optimization -- Large-scale and Big Optimization Based on Hadoop -- Computational Approaches in Large-Scale Unconstrained Optimization -- Numerical Methods for Large-Scale Nonsmooth Optimization -- Metaheuristics for Continuous Optimization of High-Dimensional Problems: State of the Art and Perspectives -- Convergent Parallel Algorithms for Big Data Optimization Problems.

The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.

ISBN: 9783319302652

Standard No.: 10.1007/978-3-319-30265-2doiSubjects--Topical Terms:

2045508
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

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245 0 0 $a Big data optimization $h [electronic resource] : $b recent developments and challenges / $c edited by Ali Emrouznejad.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xv, 487 p. : $b ill. (some col.), digital ; $c 24 cm.
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505 0 $a Big data: Who, What and Where? Social, Cognitive and Journals Map of Big Data Publications with Focus on Optimization -- Setting up a Big Data Project: Challenges, Opportunities, Technologies and Optimization -- Optimizing Intelligent Reduction Techniques for Big Data -- Performance Tools for Big Data Optimization -- Optimising Big Images -- Interlinking Big Data to Web of Data -- Topology, Big Data and Optimization -- Applications of Big Data Analytics Tools for Data Management -- Optimizing Access Policies for Big Data Repositories: Latency Variables and the Genome Commons -- Big Data Optimization via Next Generation Data Center Architecture -- Big Data Optimization within Real World Monitoring Constraints -- Smart Sampling and Optimal Dimensionality Reduction of Big Data Using Compressed Sensing -- Optimized Management of BIG Data Produced in Brain Disorder Rehabilitation -- Big Data Optimization in Maritime Logistics -- Big Network Analytics Based on Nonconvex Optimization -- Large-scale and Big Optimization Based on Hadoop -- Computational Approaches in Large-Scale Unconstrained Optimization -- Numerical Methods for Large-Scale Nonsmooth Optimization -- Metaheuristics for Continuous Optimization of High-Dimensional Problems: State of the Art and Perspectives -- Convergent Parallel Algorithms for Big Data Optimization Problems.
520 $a The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.
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