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Principles of Big Data


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

Table of Contents

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

About the Author

Jules Berman holds two bachelor of science degrees from MIT (Mathematics, and Earth and Planetary Sciences), a PhD from Temple University, and an MD, from the University of Miami. He was a graduate researcher in the Fels Cancer Research Institute, at Temple University, and at the American Health Foundation in Valhalla, New York. His post-doctoral studies were completed at the U.S. National Institutes of Health, and his residency was completed at the George Washington University Medical Center in Washington, D.C. Dr. Berman 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 worked and consulted on Big Data projects. In 2006, Dr. Berman was President of the Association for Pathology Informatics. In 2011 he received the Lifetime Achievement Award from the Association for Pathology Informatics. He is a co-author on hundreds of scientific publications. Today Dr. Berman is a free-lance author, writing extensively in his three areas of expertise: informatics, computer programming, and cancer biology. A complete list of his publications is available at As a Program Director at the National Cancer Institute, Dr. Berman directed a multi-institutional Big Data project and actively organized and participated in high-level conferences and meetings where Big Data efforts were planned. He made a number of contributions to the field, particularly in the areas of identification, de-identification, data exchange protocols, standards development, regulatory/legal issues, and metadata annotation. Aside from his personal experiences, he is a serious scholar of the subject and has studied the works of many other authors who have dealt with the many pitfalls in Big Data creation and analysis. He aims to provide readers with a balanced perspective of Big Data, that represents the views held by leaders in this multi-disciplined field.


"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).", October 3, 2013

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