Big Data Analytics: Systems, Algorithms, Applications

Front Cover
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.
 

Contents

1 Big Data Analytics
1
2 Intelligent Systems
25
3 Analytics Models for Data Science
47
4 Big Data ToolsHadoop Ecosystem Spark and NoSQL Databases
83
5 Predictive Modeling for Unstructured Data
166
6 Machine Learning Algorithms for Big Data
195
7 Social Semantic Web Mining and Big Data Analytics
217
8 Internet of Things IOT and Big Data Analytics
232
Traffic Management
347
Technical Features
348
Cisco
349
JPMorgan Chase
352
Appendices
355
Measures of Central Tendency
356
Median
357
Geometric Mean
358

9 Big Data Analytics for Financial Services and Banking
249
10 Big Data Analytics Techniques in Capital Market Use Cases
257
11 Big Data Analytics for Insurance
267
12 Big Data Analytics in Advertising
271
13 Big Data Analytics in Bioinformatics
275
14 Big Data Analytics and Recommender Systems
287
15 Security in Big Data
301
16 Privacy and Big Data Analytics
311
17 Emerging Research Trends and New Horizons
317
Case Studies
332
General Electric GE
334
Microsoft
335
Nokia
336
Facebook
337
Kaggle
338
Deutsche Bank
339
Health Sector Analytics
340
Online Insurance
341
Delta Airlines
342
LinkedIn
345
Range
359
The Mean Deviation or Average Deviation
360
Standard Deviation
361
Deviation Taken from Assumed Mean
362
Variance
363
Types of Correlation
364
Methods of Studying Correlation
365
Graphic Method
367
Regression
368
Types of Variables
369
χ2 Test ChiSquare Test
370
ChiSquare Distribution Curve
371
Conditions for Applying χ2 Test
372
Estimations
374
Hypothesis Testing
375
The Gaussian or Normal Distribution
376
Probability
378
R Language
400
R Scripts
407
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About the author (2019)

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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