Cover
Title Page
Copyright
ABOUT THE AUTHOR
TABLE OF CONTENTS
List of Abbreviations
Preface
Chapter 1 Introduction to Big Data
1.1. Introduction
1.2. Concept of Big Data
1.3. What is Data?
1.4. What is Big Data?
1.5. The Big Data Systems are Different
1.6. Big Data Analytics
1.7. Case Study: German Telecom Company
1.8. Checkpoints
Chapter 2 Identifier Systems
2.1. Meaning Of Identifier System
2.2. Features Of An Identifier System
2.3. Database Identifiers
2.4. Classes Of Identifiers
2.5. Rules For Regular Identifiers
2.6. One-Way Hash Function
2.7. De-Identification And Data Scrubbing
2.8. Concept Of De-Identification
2.9. The Process Of De-Identifications
2.10. Techniques Of De-Identification
2.11. Assessing The Risk Of Re-Identification
2.12. Case Study: Mastercard: Applying Social Media Research Insights For Better Business Decisions
2.13. Checkpoints
Chapter 3 Improving the Quality of Big Data and Its Measurement
3.1. Data Scrubbing
3.2. Meaning of Bad Data
3.3. Common Approaches to Improve Data Quality
3.4. Measuring Big Data
3.5. How To Measure Big Data
3.6. Measuring Big Data Roi: A Sign of Data Maturity
3.7. The Interplay Of Hard And Soft Benefits
3.8. When Big Data Projects Require Big Investments
3.9. Real-Time, Real-World Roi
3.10. Case Study 2: Southwest Airlines: Big Data Pr Analysis Aids on-Time Performance
3.11. Checkpoints
Chapter 4 Ontologies
Introduction
4.1. Concept of Ontologies
4.2. Relation of Ontologies To Big Data Trend
4.3. Advantages And Limitations of Ontologies
4.4. Why Are Ontologies Developed?
4.5. Semantic Web
4.6. Major Components of Semantic Web
4.7. Checkpoints
Chapter 5 Data Integration and Interoperability
5.1. What Is Data Integration?
5.2. Data Integration Areas
5.3. Types of Data Integration
5.4. Challenges of Data Integration and Interoperability in Big Data
5.5. Challenges of Big Data Integration And Interoperability
5.6. Immutability And Immortality
5.7. Data Types and Data Objects
5.8. Legacy Data
5.9. Data Born From Data
5.10. Reconciling Identifiers Across Institutions
5.11. Simple But Powerful Business Data Techniques
5.12. Association Rule Learning (ARL)
5.13. Classification Tree Analysis
5.14. Checkpoints
Chapter 6 Clustering, Classification, and Reduction
Introduction
6.1. Logistic Regression (Predictive Learning Model)
6.2. Clustering Algorithms
6.3. Data Reduction Strategies
6.4. Data Reduction Methods
6.5. Data Visualization: Data Reduction For Everyone
6.6. Case Study: Coca-Cola Enterprises (CCE) Case Study: The Thirst For Hr Analytics Grows
6.5. Checkpoints
Chapter 7 Key Considerations in Big Data Analysis
Introduction
7.1. Major Considerations For Big Data And Analytics
7.2. Overfitting
7.3. Bigness Bias
7.4. Step Wise Approach In Analysis of Big Data
7.5. Complexities In Big Data
7.6. The Importance
7.7. Dimensions of Data Complexities
7.8. Complexities Related To Big Data
7.9. Complexity Is Killing Big Data Deployments
7.10. Methods That Facilitate In Removal of Complexities
7.11. Case Study: Cisco Systems, Inc.: Big Data Insights Through Network Visualization
7.12. Checkpoints
Chapter 8 The Legal Obligation
8.1. Legal Issues Related to Big Data
8.2. Controlling The Use of Big Data
8.3. 3 Massive Societal Issues of Big Data
8.4. Social Issues That Big Data Helps In Addressing
8.5. Checkpoints
Chapter 9 Applications of Big Data and Its Future
9.1. Big Data In Healthcare Industry
9.2. Big Data In Government Sector
9.3. Big Data In Media And Entertainment Industry
9.4. Big Data In Weather Patterns
9.5. Big Data In Transportation Industry
9.6. Big Data In Banking Sector
9.7. Application Of Big Data: Internet Of Things
9.8. Education
9.9. Retail And Wholesale Trade
9.10. The Future
9.11. The Future Trends Of The Big Data
9.12. Will Big Data, Being Computationally Complex, Require A New Generation Of Supercomputers?
7.13. Conclusion
9.14. Checkpoints
Index
Back Cover