Principles of Big Data book cover

Principles of Big Data

Preparing, Sharing, and Analyzing Complex Information

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.

Audience

data managers, data analysts, statisticians

Paperback, 288 Pages

Published: May 2013

Imprint: Morgan Kaufmann

ISBN: 978-0-12-404576-7

Reviews

  • "The author has produced a sober, serious treatment of this emerging phenomenon, avoiding hype and gee-whiz cases in favor of concepts and mature advice. For example, the author offers ten distinctions between big data and small data, including such factors as goals, location, data structure, preparation, and longevity. This characterization provides much greater insight into the phenomenon than the standard 3V treatment (volume, velocity, and variety)."--ComputingReviews.com, October 3, 2013


Contents

  • Preface

    Introduction

    1. Big Data Moves to the Center of the Universe

    2. Measurement

    3. Annotation

    4. Identification, De-identification, and Re-identification

    5. Ontologies and Semantics: How information is endowed with meaning

    6. Standards and their Versions

    7. Legacy Data

    8. Hypothesis Testing

    9. Prediction

    10. Software

    11. Complexity

    12. Vulnerabilities

    13. Legalities

    14. Social and Ethical Issues

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