目次

1. Introduction 2. Providing Structure to Unstructured Data 3. Identification, Deidentification, and Reidentification 4. Metadata, Semantics, and Triples 5. Classifications and Ontologies 6. Introspection 7. Data Integration and Software Interoperability 8. Immutability and Immortality 9. Assessing the Adequacy of a Big Data Resource 10. Measurement 11. Indispensable Tips for Fast and Simple Big Data Analysis 12. Finding the Clues in Large Collections of Data 13. Using Random Numbers to Bring Your Big Data Analytic Problems Down to Size 14. Special Considerations in Big Data Analysis 15. Big Data Failures and How to Avoid (Some of) Them 16. Legalities 17. Data Sharing 18. Data Reanalysis: Much More Important than Analysis 19. Repurposing Big Data