Big data analytics: systems, algorithms, applications

By: Prabhu, C. S. RContributor(s): Chivukula, Aneesh Sreevallabh | Mogadala, AdityaMaterial type: TextTextPublication details: Switzerland Springer 2019 Description: xxvi, 412 pISBN: 9789811500961Subject(s): Big data | Data miningDDC classification: 005.7 Summary: 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.
List(s) this item appears in: IT & Decision Sciences | HR & OB
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Item type Current library Collection Call number Copy number Status Date due Barcode
Book Book Indian Institute of Management LRC
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IT & Decisions Sciences 005.7 PRA (Browse shelf(Opens below)) 1 Available 002768

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.

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