Principles of Big Data /
| 主要作者: | |
|---|---|
| 格式: | Licensed eBooks |
| 語言: | 英语 |
| 出版: |
Ashland :
Arcler Press,
2020.
|
| 在線閱讀: | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2725218 |
書本目錄:
- 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