000 04052nam a22005415i 4500
001 978-3-319-30265-2
003 DE-He213
005 20220801220846.0
007 cr nn 008mamaa
008 160526s2016 sz | s |||| 0|eng d
020 _a9783319302652
_9978-3-319-30265-2
024 7 _a10.1007/978-3-319-30265-2
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aBig Data Optimization: Recent Developments and Challenges
_h[electronic resource] /
_cedited by Ali Emrouznejad.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXV, 487 p. 182 illus., 160 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Big Data,
_x2197-6511 ;
_v18
505 0 _aBig 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 _aThe 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.
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 0 _aOperations research.
_912218
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aOperations Research and Decision Theory.
_931599
700 1 _aEmrouznejad, Ali.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_952772
710 2 _aSpringerLink (Online service)
_952773
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319302638
776 0 8 _iPrinted edition:
_z9783319302645
776 0 8 _iPrinted edition:
_z9783319807652
830 0 _aStudies in Big Data,
_x2197-6511 ;
_v18
_952774
856 4 0 _uhttps://doi.org/10.1007/978-3-319-30265-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79016
_d79016