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Principles of Big Data
 
 

Principles of Big Data, 1st Edition

Preparing, Sharing, and Analyzing Complex Information

 
Principles of Big Data, 1st Edition,Jules Berman,ISBN9780124045767
 
 
 

  

Morgan Kaufmann

9780124045767

9780124047242

288

235 X 191

Learn simple, but powerful methods that permit data to be shared and integrated among different big Data resources.

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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.

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.

Readership

data managers, data analysts, statisticians

Jules Berman

Jules Berman Jules Berman, M.D., Ph.D served as Chief of Anatomic Pathology, Surgical Pathology and Cytopathology at the Veterans Administration Medical Center in Baltimore, Maryland, where he held joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. In 1998, he became the Program Director for Pathology Informatics in the Cancer Diagnosis Program at the U.S. National Cancer Institute, where he directed a multi-institutional Big Data project. He made a number of contributions to the informatics field, particularly in the areas of identification, de-identification, data exchange protocols, standards development, regulatory/legal issues, and metadata annotation. In 2011 he received the Lifetime Achievement Award from the Association for Pathology Informatics. Today Dr. Berman (h-index 19) is a free-lance author, writing extensively in his three areas of expertise: informatics, computer programming, and disease biology He’s the author of several Elsevier titles including; Rare Diseases and Orphan Drugs (2014), Principles of Big Data (2013) and Taxonomic Guide to Infectious Diseases (2012),

Affiliations and Expertise

Ph.D., M.D., freelance author with expertise in informatics, computer programming, and cancer biology, Columbia, MD, USA

View additional works by Jules J. Berman

Principles of Big Data, 1st Edition

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

Quotes and reviews

"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."--ODBMS.org, March 21, 2014
"The book is written in a colloquial style and is full of anecdotes, quotations from famous people, and personal opinions."--ComputingReviews.com, February 3, 2014
"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

 
 

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