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000742321 020__ $$a9783319253138$$qelectronic book
000742321 020__ $$a3319253131$$qelectronic book
000742321 020__ $$z9783319253114
000742321 0247_ $$a10.1007/978-3-319-25313-8$$2doi
000742321 035__ $$aSP(OCoLC)ocn935321269
000742321 035__ $$aSP(OCoLC)935321269
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000742321 049__ $$aISEA
000742321 050_4 $$aQA76.585
000742321 08204 $$a004.67/82$$223
000742321 24500 $$aBig-data analytics and cloud computing$$h[electronic resource] :$$btheory, algorithms and applications /$$cMarcello Trovati, Richard Hill, Ashiq Anjum, Shao Ying Zhu, Lu Liu, editors.
000742321 264_1 $$aCham :$$bSpringer,$$c2015.
000742321 300__ $$a1 online resource (xvi, 169 pages) :$$bcolor illustrations.
000742321 336__ $$atext$$btxt$$2rdacontent
000742321 337__ $$acomputer$$bc$$2rdamedia
000742321 338__ $$aonline resource$$bcr$$2rdacarrier
000742321 500__ $$aIncludes index.
000742321 506__ $$aAccess limited to authorized users.
000742321 520__ $$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.
000742321 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 20, 2016).
000742321 650_0 $$aCloud computing.
000742321 650_0 $$aBig data.
000742321 7001_ $$aTrovati, Marcello,$$eeditor.
000742321 7001_ $$aHill, Richard$$c(Professor of computing),$$eeditor.
000742321 7001_ $$aAnjum, Ashiq,$$eeditor.
000742321 7001_ $$aZhu, Shao Ying,$$eeditor.
000742321 7001_ $$aLiu, Lu,$$d1980-$$eeditor.
000742321 85280 $$bebk$$hSpringerLink
000742321 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-25313-8$$zOnline Access$$91397441.1
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000742321 980__ $$aEBOOK
000742321 980__ $$aBIB
000742321 982__ $$aEbook
000742321 983__ $$aOnline
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