Big Data Analytics: Systems, Algorithms, Applications

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About this book

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.

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Table of contents (17 chapters)

Front Matter

Pages i-xxvi

Big Data Analytics

Intelligent Systems

Pages 25-46

Analytics Models for Data Science

Pages 47-82

Big Data Tools—Hadoop Ecosystem, Spark and NoSQL Databases

Pages 83-165

Predictive Modeling for Unstructured Data

Pages 167-194

Machine Learning Algorithms for Big Data

Pages 195-215

Social Semantic Web Mining and Big Data Analytics

Pages 217-231

Internet of Things (IOT) and Big Data Analytics

Pages 233-247

Big Data Analytics for Financial Services and Banking

Pages 249-256

Big Data Analytics Techniques in Capital Market Use Cases

Pages 257-265

Big Data Analytics for Insurance

Pages 267-270

Big Data Analytics in Advertising

Pages 271-274

Big Data Analytics in Bio-informatics

Pages 275-286

Big Data Analytics and Recommender Systems

Pages 287-299

Security in Big Data

Pages 301-309

Privacy and Big Data Analytics

Pages 311-315

Emerging Research Trends and New Horizons

Pages 317-331

Back Matter

Pages 333-412

Authors and Affiliations

National Informatics Centre, New Delhi, India

Advanced Analytics Institute, University of Technology, Sydney, Ultimo, Australia

Saarland University, Saarbrücken, Germany

Qure.ai, Goregaon East, Mumbai, India

School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India

About the authors

Dr. Chivukula Sree Rama Prabhu has held prestigious positions with Government of India and various institutions. He retired as Director General of the National Informatics Centre (NIC), Ministry of Electronics and Information Technology, Government of India, New Delhi, and has worked with Tata Consultancy Services (TCS), CMC, TES and TELCO (now Tata Motors). He was also faculty for the Programs of the APO (Asian Productivity Organization). He has taught and researched at the University of Central Florida, Orlando, USA, and also had a brief stint as a Consultant to NASA. He was Chairman of the Computer Society of India (CSI), Hyderabad Chapter. He is presently working as an Advisor (Honorary) at KL University, Vijayawada, Andhra Pradesh, and as a Director of Research and Innovation at Keshav Memorial Institute of Technology (KMIT), Hyderabad.
He received his Master’s degree in Electrical Engineering with specialization in Computer Science from the Indian Institute of Technology, Bombay. He has guided many Master’s and doctoral students in research areas such as Big Data.Dr. Aneesh Sreevallabh Chivukula is currently a Research Scholar at the Advanced Analytics Institute, University of Technology Sydney (UTS), Australia. Previously, he chiefly worked in computational data science-driven product development at Indian startup companies and research labs. He received his M.S. degree from the International Institute of Information Technology (IIIT), Hyderabad. His research interests include machine learning, data mining, pattern recognition, big data analytics and cloud computing.
Dr. Aditya Mogadala is a postdoc in the Language Science and Technology at Saarland University. His research concentrates on the general area of Deep/Representation learning applied for integration of external real-world/common-sense knowledge (e.g., vision and knowledge graphs) into natural language sequence generation models. Before Postdoc, he was a PhD student and Research Associate at the Karlsruhe Institute of Technology, Germany. He holds B.Tech and M.S. degree from the IIIT, Hyderabad, and has worked as a Software Engineer at IBM India Software Labs.
Mr. Rohit Ghosh currently works at Qure, Mumbai. He previously served as a Data Scientist for ListUp, and for Data Science Labs. Holding a B.Tech. from the IIT Mumbai, his work involves R&D areas in computer vision, deep learning, reinforcement learning (mostly related to trading strategies) and cryptocurrencies.
Dr. Jenila Livingston is an Associate Professor with the CSE Dept at VIT, Chennai. Her teaching foci and research interests include artificial intelligence, soft computing, and analytics.

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