000			04000nam a22005415i 4500
001			978-3-319-25313-8
003			DE-He213
005			20200421111853.0
007			cr nn 008mamaa
008			160112s2015 gw | s |||| 0|eng d
020			_a9783319253138 _9978-3-319-25313-8
024	7		_a10.1007/978-3-319-25313-8 _2doi
050		4	_aQA276-280
072		7	_aUYAM _2bicssc
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072		7	_aCOM077000 _2bisacsh
082	0	4	_a005.55 _223
245	1	0	_aBig-Data Analytics and Cloud Computing _h[electronic resource] : _bTheory, Algorithms and Applications / _cedited by Marcello Trovati, Richard Hill, Ashiq Anjum, Shao Ying Zhu, Lu Liu.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015.
300			_aXVI, 169 p. 67 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
520			_aThis important and timely text/reference reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: Describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures Examines the applications and implementations that utilize big data in cloud architectures Surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions Identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches Provides relevant theoretical frameworks, empirical research findings, and numerous case studies Discusses real-world applications of algorithms and techniques to address the challenges of big datasets This authoritative volume will be of great interest to researchers, enterprise architects, business analysts, IT infrastructure managers and application developers, who will benefit from the valuable insights offered into the adoption of architectures for big data and cloud computing. The work is also suitable as a textbook for university instructors, with the outline for a possible course structure suggested in the preface. The editors are all members of the Computing and Mathematics Department at the University of Derby, UK, where Dr. Marcello Trovati serves as a Senior Lecturer in Mathematics, Dr. Richard Hillas a Professor and Head of the Computing and Mathematics Department, Dr. Ashiq Anjum as a Professor of Distributed Computing, Dr. Shao Ying Zhu as a Senior Lecturer in Computing, and Dr. Lu Liu as a Professor of Distributed Computing. The other publications of the editors include the Springer titles Guide to Security Assurance for Cloud Computing, Guide to Cloud Computing and Cloud Computing for Enterprise Architectures.
650		0	_aComputer science.
650		0	_aComputer communication systems.
650		0	_aMathematical statistics.
650		0	_aComputer science _xMathematics.
650		0	_aComputer simulation.
650	1	4	_aComputer Science.
650	2	4	_aProbability and Statistics in Computer Science.
650	2	4	_aComputer Communication Networks.
650	2	4	_aSimulation and Modeling.
650	2	4	_aMath Applications in Computer Science.
700	1		_aTrovati, Marcello. _eeditor.
700	1		_aHill, Richard. _eeditor.
700	1		_aAnjum, Ashiq. _eeditor.
700	1		_aZhu, Shao Ying. _eeditor.
700	1		_aLiu, Lu. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319253114
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-25313-8
912			_aZDB-2-SCS
942			_cEBK
999			_c56265 _d56265