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Chapter 1 - Providing Structure to Unstructured Data
Pages 1-14 - Book chapterAbstract only
Chapter 2 - Identification, Deidentification, and Reidentification
Pages 15-33 - Book chapterAbstract only
Chapter 3 - Ontologies and Semantics
Pages 35-48 - Book chapterAbstract only
Chapter 4 - Introspection
Pages 49-61 - Book chapterAbstract only
Chapter 5 - Data Integration and Software Interoperability
Pages 63-75 - Book chapterAbstract only
Chapter 6 - Immutability and Immortality
Pages 77-87 - Book chapterAbstract only
Chapter 7 - Measurement
Pages 89-98 - Book chapterAbstract only
Chapter 8 - Simple but Powerful Big Data Techniques
Pages 99-127 - Book chapterAbstract only
Chapter 9 - Analysis
Pages 129-144 - Book chapterAbstract only
Chapter 10 - Special Considerations in Big Data Analysis
Pages 145-155 - Book chapterAbstract only
Chapter 11 - Stepwise Approach to Big Data Analysis
Pages 157-165 - Book chapterAbstract only
Chapter 12 - Failure
Pages 167-182 - Book chapterAbstract only
Chapter 13 - Legalities
Pages 183-199 - Book chapterAbstract only
Chapter 14 - Societal Issues
Pages 201-215 - Book chapterAbstract only
Chapter 15 - The Future
Pages 217-227 - Book chapterNo access
Glossary
Pages 229-245 - Book chapterNo access
References
Pages 247-255 - Book chapterNo access
Index
Pages 257-261
About the book
Description
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.
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.
Key Features
- 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
- 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
Details
ISBN
978-0-12-404576-7
Language
English
Published
2013
Copyright
Copyright © 2013 Elsevier Inc. All rights reserved.
Imprint
Morgan Kaufmann