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.
L'auteur Jules Berman
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 http://www.julesberman.info/pubs.htm 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.
Affiliations and Expertise ,Ph.D., M.D., is a free-lance author, writing extensively in his three areas of expertise: informatics, computer programming, and cancer biology. Ph.D., M.D.
- 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
Caractéristiques techniques du livre "Principles of big data"
|Nb. de pages||288|
|Format||23 x 19|
|Intérieur||Noir et Blanc|
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- Informatique Bases de données
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- Sciences Mathématiques Mathématiques par matières Analyse Analyse numérique
- Sciences Mathématiques Mathématiques appliquées Mathématiques pour les sciences de la vie Biostatistiques
- Sciences Mathématiques Mathématiques appliquées Méthodes numériques
- Sciences Mathématiques Mathématiques appliquées Statistiques Analyse de données