Big Data Analytics: Systems, Algorithms, Applications, 1st ed. 2019

Table of contents:

Preface

Chapter 1 Big Data

1.1       Introduction

1.2       What is Big Data?

1.3       Disruptive change and paradigm shift and its business meaning

1.4       HADOOP

1.4.2    Big Bang of Big Data

1.4.3    Possibilities

1.4.4    Future

1.4.5    Parallel processing for problem solving

1.4.7    Hadoop and HDFS

1.4.8    Hadoop Version 1.0 & 2.0

     1.4.8.1 Limitations of Hadoop 1.0

1.4.9    Hadoop 2.0

1.5       HDFS Overview

1.5.1    Map Reduce framework

1.5.2    Job Tracker

1.5.3    YARN

1.6       Hadoop Eco System

1.6.1    Cloud based Hadoop Solutions

1.6.2    SPARK and Data Stream Processing                   

1.7       Decision Making and Data Analysis in the Context of Big Data Environment

1.7.1    Present Day Data Analytics Techniques

1.8       Machine Learning Algorithms

Conclusion

Review Questions

Chapter 2: Intelligent Systems

2.2       Machine Learning Paradigms

2.2.1    Open Source Data Science

2.2.2    Machine Intelligence and Computational Intelligence

2.2.3    Data Engineering and Data Sciences

2.3       Machine Learning Paradigms

2.4       Big Data Computing

2.4.1    Distributed Systems and Database Systems

2.4.3    Ubiquitous Computing Infrastructures

Conclusion

Review Questions

References

3.1       Introduction

3.2       Applications of Predictive Modeling

3.3       Feature Engineering

3.4       Pattern Mining for Predictive Modeling

Conclusion

Review Questions

References

4.1 Introduction

4.2 Generative vs Discriminative Algorithms

4.3 Supervised Learning for Big Data

4.3.1 Decision Trees

4.3.3  Regression and Forecasting

4.3.4 Supervised Neural Networks

4.3.5 Support Vector Machines

4.4 Unsupervised Learning for Big Data

4.4.2 Principal Component Analysis (PCA)

4.4.3 Latent Dirichlet Allocation (LDA)

4.4.4 Matrix Factorization

4.4.5 Manifold Learning

4.5 Semi-Supervised Learning for Big Data

4.5.1 Co-training

4.5.2 Label Propagation

4.6 Reinforcement Learning Basics for Big Data

4.6.2 Planning

4.6.3 RL in practice

4.7 Online Learning for Big Data

Conclusion

Review Questions

References

Chapter 5: Analytics Models for Data Science

5.2       Data Models

5.2.1    Data Products

5.2.2    Data Munging

5.2.3    Descriptive Analytics

5.2.4    Predictive Analytics

5.2.5    Data Science

5.2.6    Network Science

5.3.1    Data Structures for Big Data

5.3.2    Feature Engineering for Structured Data

            5.3.2.1 Feature Construction

            5.3.2.2 Feature Extraction

            5.3.2.3 Feature Selection

            5.3.2.4 Feature Learning

            5.3.2.5 Ensemble Learning

5.3.3    Computational Algorithmics

5.4.1 Parallel Programming

5.4.2 Functional Programming

5.4.3 Distributed Programming

Conclusion

Review Questions

References

Chapter 6: Social Semantic Web Mining and Big Data Analytics

6.1       Introduction

6.2       What is Semantic Web?

6.3       Knowledge Representation Techniques and Platforms in Semantic Web

6.4       Web Ontology Language (OWL)

6.5       Object Knowledge Model (OKM)

6.7       Architecture of Semantic Web and the Semantic Web Road Map

6.8       Social Semantic Web Mining

6.9       Conceptual Networks and Folksonomies or Folk Taxonomies of Concepts/Sub Concepts

6.10     SNA and ABM

6.11     e-Social Science

6.12     Opinion Mining and Sentiment Analysis

Conclusion

Review Questions

References

Chapter 7: Internet of Things (IoT) and Big Data Analytics

7.1       Introduction

7.2       Smart Cities and IOT Sectoral Applications

7.3       Stages of IOT And Stakeholders

7.3.1    Stages of IOT

7.3.2    Stakeholders

7.4       Analytics

7.4.1    Analytics from the Edge to Cloud

7.5       Access

7.6       Cost Reduction

7.7       Opportunities and Business Model

7.8       Content and Semantics

7.9       Data base Business models coming out of IOT

7.10     Future of IOT

            7.10.1 Technology Drivers

            7.10.2  Future possibilities

7.10.3 Challenges and Concerns

7.11     Big Data Analytics and IOT

7.11.1 Infrastructure for integration of Big Data with IoT

7.12     Fog Computing and Fog Analytics

7.13     Research Trends

Conclusion

Review Questions

References

Chapter 8: Big Data Analytics for Financial Services and Banking

8.1       Introduction

8.3       Sentiment Analysis for consolidating customer feedback.

8.4       Predictive Analytics for capitalizing on customer insights

8.5       Model building

8.6       Fraud detection and Risk management

8.7       Integration of Big Data Analytics into operations.

8.8       How banks can benefit from Big Data Analytics?

8.9       Best practices of Data Analytics in banking for crises redressal and management

8.10     Bottlenecks

Conclusion

Review Questions

References

Chapter 9: Big Data Analytics Techniques in Capital Market Use Cases

9.1       Introduction

9.2       Capital Market Use Cases of Big Data Technologies

9.2.1    Algorithmic Trading

9.2.2    Investors’ Faster Access to Securities

9.3       Prediction Algorithms

9.3.2    Efficient Market Hypothesis

9.3.3    Random Walk Theory (RWT)

9.3.4    Trading Philosophies

9.3.5    Simulation Techniques

9.5       Experimental Analysis using Bag of Words and Support Vector Machine (SVM) Application to News Articles

9.7       Named Entities

9.8       Object Knowledge Model (OKM)

9.9       Application of Machine Learning Algorithms

9.9.1    Sources of Data

9.10     Future Work

Conclusion

Review Questions

References

Chapter 10: Big Data Analytics for Insurance

10.1     Introduction

10.2     The Insurance Business Scenario

10.3     Big Data Deployment in Insurance

10.4     Insurance Use Cases

10.5     Customer Needs Analysis

10.6     Other Applications

Conclusion

Review Questions

References

11.1     Introduction

11.2     Ills of Social Networking – Identity Theft

11.3     Organizational Big Data Security

11.4     Security in Hadoop

11.5     Issues and Challenges in Big Data Security

11.6     Encryption for Security

11.7     Secure Map -Reduce and Log Management

11.8     Access Control, Differential Privacy and Third Party Authentication

11.9     Real Time Access Control

11.10   Security Best Practices for Non-Relational Or NoSQL Databases                                     11.11      Challenges, Issues and New Approaches

11.12   Research Overview and New Approaches for Security Issues in Big Data 

Conclusion

Review Questions

References

12.1     Introduction

12.2     Privacy Protection

12.3     Enterprise Big Data Privacy Policy And COBIT

12.4     Assurance and Governance

12.5     Challenges, Issues and Approaches For Privacy Protection

Conclusion

Review Questions

References

Chapter 13: Emerging Trends in Big Data

13.1 Learning to Generate

            13.1.1 Unstructured Data

            13.1.2 Structured Data

13.2 Learning to Learn

            13.2.1 Model Selection

            13.2.2 Bayesian Optimization

            13.2.3 Transfer Learning

13.3 Adversarial Learning

            13.3.1 Anomaly Detection

            13.3.2 Concept Drift

            13.3.3 Adversarial Networks

13.4 Complex Networks

            13.3.1 Graph Databases

            13.3.2 Knowledge Graphs

            13.3.3 Event Mining

            13.3.3 Dynamic Network Analysis

Conclusion

Review Questions

References

Case Studies

1.      Google

3.      Microsoft

4.      Nokia

5.      Facebook

7.      Kaggle

8.      Deutsche Bank

9.      Health Sector Analysis

11.  Delta Airlines

12.  Linked.In

13.  Traffic Management

Appendix 1: Databases for Big Data: NoSQL Databases Column Databases and Graph Databases

Appendix 2:  HDFS and MapReduce

Appendix 3:  Statistical Foundations

Appendix 4:  Probability, Random variables and mathematical expectation

Appendix 5:  R Language Features and Its Applications in Machine Learning (Clustering,

                       Classification and Regression)

Appendix 6: Spark and Scala for data streams