Principles and Practice of Big Data

Jules Berman holds two Bachelor of Science degrees from MIT (in Mathematics and in Earth and Planetary Sciences), a PhD from Temple University, and an MD from the University of Miami. He was a graduate researcher at the Fels Cancer Research Institute (Temple University) and at the American Health Foundation in Valhalla, New York. He completed his postdoctoral studies at the US National Institutes of Health, and his residency at the George Washington University Medical Center in Washington, DC. 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 transferred to the US National Institutes of Health as a Medical Officer and as the Program Director for Pathology Informatics in the Cancer Diagnosis Program at the National Cancer Institute. Dr. Berman is a past President of the Association for Pathology Informatics and is the 2011 recipient of the Association’s Lifetime Achievement Award. He is a listed author of more than 200 scientific publications and has written more than a dozen books in his three areas of expertise: informatics, computer programming, and pathology. Dr. Berman is currently a freelance writer.

1. Introduction2. Providing Structure to Unstructured Data3. Identification, Deidentification, and Reidentification4. Metadata, Semantics, and Triples5. Classifications and Ontologies6. Introspection7. Data Integration and Software Interoperability8. Immutability and Immortality9. Assessing the Adequacy of a Big Data Resource10. Measurement11. Indispensable Tips for Fast and Simple Big Data Analysis12. Finding the Clues in Large Collections of Data13. Using Random Numbers to Bring Your Big Data Analytic Problems Down to Size14. Special Considerations in Big Data Analysis15. Big Data Failures and How to Avoid (Some of) Them16. Legalities17. Data Sharing18. Data Reanalysis: Much More Important than Analysis19. Repurposing Big Data