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Big Data Analytics: Systems, Algorithms, Applications

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Big Data Analytics: Systems, Algorithms, Applications/ by C.S.R. Prabhu, Aneesh Sreevallabh Chivukula, Aditya Mogadala, Rohit Ghosh, L.M. Jenila Livingston.
Author:	Prabhu, C.S.R.
other author:	Chivukula, Aneesh Sreevallabh.
Description:	XXVI, 412 p. 174 illus., 108 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Big data. -
Online resource:	https://doi.org/10.1007/978-981-15-0094-7
ISBN:	9789811500947

Big Data Analytics: Systems, Algorithms, Applications
Prabhu, C.S.R.

Big Data Analytics: Systems, Algorithms, Applications[electronic resource] /by C.S.R. Prabhu, Aneesh Sreevallabh Chivukula, Aditya Mogadala, Rohit Ghosh, L.M. Jenila Livingston. - 1st ed. 2019. - XXVI, 412 p. 174 illus., 108 illus. in color.online resource.

Big 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.

This 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.

ISBN: 9789811500947

Standard No.: 10.1007/978-981-15-0094-7doiSubjects--Topical Terms:

981821
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Big Data Analytics: Systems, Algorithms, Applications
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