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024 7 _a10.1007/978-981-15-0094-7
_2doi
050 4 _aQA76.9.B45
072 7 _aUN
_2bicssc
072 7 _aCOM021000
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072 7 _aUN
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100 1 _aPrabhu, C.S.R.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aBig Data Analytics: Systems, Algorithms, Applications
_h[electronic resource] /
_cby C.S.R. Prabhu, Aneesh Sreevallabh Chivukula, Aditya Mogadala, Rohit Ghosh, L.M. Jenila Livingston.
250 _a1st ed. 2019.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2019.
300 _aXXVI, 412 p. 174 illus., 108 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aBig Data -- Intelligent Systems -- Analytics Models for Data Science -- Big Data Tools – Hadoop Eco System -- Predictive Modeling for Unstructured Data -- Machine Learning Algorithms for Big Data -- Social Semantic Web Mining and Big Data Analytics -- Internet of Things (IoT) and Big Data Analytics -- Big Data Analytics for Financial and Services Banking -- Big Data Analytics Techniques in Capital Market Use Cases.
520 _aThis book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn,the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.
650 0 _aBig data.
650 0 _aData mining.
650 1 4 _aBig Data.
650 2 4 _aData Mining and Knowledge Discovery.
700 1 _aChivukula, Aneesh Sreevallabh.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aMogadala, Aditya.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aGhosh, Rohit.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aLivingston, L.M. Jenila.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811500930
776 0 8 _iPrinted edition:
_z9789811500954
776 0 8 _iPrinted edition:
_z9789811500961
856 4 0 _uhttps://doi.org/10.1007/978-981-15-0094-7
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c173102
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