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001			85691
003			ES-MaUEC
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008			160526s2016 gw | s |||| 0|eng d
020			_a9783319302652
040			_aES-MaUEC
082	0	4	_a006.3
050		4	_aQA76.9.B45 _bB543 2016 EB
245	1	0	_aBig Data Optimization: Recent Developments and Challenges _cedited by Ali Emrouznejad
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XV, 487 páginas) _b182 ilustraciones, 160 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aStudies in Big Data _x2197-6503 _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.
710	2		_aSpringerLink (Online service) _0Local _9106996
830		0	_aMcGraw-Hill's AccessMedicine
942			_2lcc _cLE
988	0	0	_aEBOOK, EBSPRINGER
650	0	4	_9495511 _aDatos masivos
650		7	_aToma de decisiones _0comprobar BNE19900995115 _2embne _9141176
700	1		_aEmrouznejad, Ali _eeditor literario _0Local _998830
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-30265-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319302652
907			_a.b12949693 _b10-10-17 _c21-11-16
945			_aEBOOK EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11614201 _z30-06-17
998			_aSI _b07/2018 _cm _dz _ea _feng _gxxu _h0
999			_c85691 _d85691 _x1