Big Data Optimization: Recent Developments and Challenges (Record no. 79016)

000 -LEADER
fixed length control field 04052nam a22005415i 4500
001 - CONTROL NUMBER
control field 978-3-319-30265-2
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801220846.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 160526s2016 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319302652
-- 978-3-319-30265-2
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
245 10 - TITLE STATEMENT
Title Big Data Optimization: Recent Developments and Challenges
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2016.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XV, 487 p. 182 illus., 160 illus. in color.
490 1# - SERIES STATEMENT
Series statement Studies in Big Data,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 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 ## - SUMMARY, ETC.
Summary, etc 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.
700 1# - AUTHOR 2
Author 2 Emrouznejad, Ali.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-319-30265-2
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
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-- Springer International Publishing :
-- Imprint: Springer,
-- 2016.
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-- computer
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-- rdamedia
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-- online resource
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
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-- Operations research.
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-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
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-- Operations Research and Decision Theory.
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-- 2197-6511 ;
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