Principles of Big Data
Principles of Big Data
Morgan Kaufmann (May 2013) | ISBN: 0124045766 | PDF + EPUB | 288 pages | 6.3 MB


Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators.


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Learn general methods for specifying Big Data in a way that is understandable to humans and to computers.
Avoid the pitfalls in Big Data design and analysis.
Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources.

Table of Contents

Chapter 1. Big Data Moves to the Center of the Universe
Chapter 2. Measurement
Chapter 3. Annotation
Chapter 4. Identification, De-identification, and Re-identification
Chapter 5. Ontologies and Semantics: How information is endowed with meaning
Chapter 6. Standards and their Versions
Chapter 7. Legacy Data
Chapter 8. Hypothesis Testing
Chapter 9. Prediction
Chapter 10. Software
Chapter 11. Complexity
Chapter 12. Vulnerabilities
Chapter 13. Legalities
Chapter 14. Social and Ethical Issues


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