Big Data Analytics: Systems, Algorithms, ApplicationsSpringer Nature, 2019 M10 14 - 412 pages 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 | |
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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Common terms and phrases
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