Big data analytics: systems, algorithms, applications
Material type: TextPublication 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.Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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Book | Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 005.7 PRA (Browse shelf(Opens below)) | 1 | Available | 002768 |
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005.7 ABU Social big data analytics: | 005.7 BEA Data and society: | 005.7 KEP Mathematics of big data: | 005.7 PRA Big data analytics: systems, algorithms, applications | 005.7 SHA Big data systems: a 360-degree approach | 005.7 SUT Machine learning models and algorithms for big data classification: thinking with examples for effective learning | 005.7 TIE Data science |
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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