LEADER 04304nam 22006015 450 001 9910254222803321 005 20200703231612.0 010 $a3-319-30265-5 024 7 $a10.1007/978-3-319-30265-2 035 $a(CKB)3710000000717975 035 $a(DE-He213)978-3-319-30265-2 035 $a(MiAaPQ)EBC4533401 035 $a(PPN)194078205 035 $a(EXLCZ)993710000000717975 100 $a20160526d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBig Data Optimization: Recent Developments and Challenges /$fedited by Ali Emrouznejad 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XV, 487 p. 182 illus., 160 illus. in color.) 225 1 $aStudies in Big Data,$x2197-6503 ;$v18 311 $a3-319-30263-9 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $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. 330 $aThe main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book. 410 0$aStudies in Big Data,$x2197-6503 ;$v18 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aOperations research 606 $aDecision making 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aOperations research. 615 0$aDecision making. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aOperations Research/Decision Theory. 676 $a005.7 702 $aEmrouznejad$b Ali$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254222803321 996 $aBig Data Optimization$91541088 997 $aUNINA