Front Cover -- Principles of Big Data: Preparing,Sharing,and Analyzing Complex Information -- Copyright -- Dedication -- Contents -- Acknowledgments -- Author Biography -- Preface -- Introduction -- Definition of Big Data -- Big Data Versus Small Data -- Whence Comest Big Data? -- The Most Common Purpose of Big Data is to Produce Small Data -- Opportunities -- Big Data Moves to the Center of the Information Universe -- Chapter 1: Providing Structure to Unstructured Data -- Background -- Machine Translation -- Autocoding -- Indexing -- Term Extraction -- Chapter 2: Identification, Deidentification, and Reidentification -- Background -- Features of an Identifier System -- Registered Unique Object Identifiers -- Really Bad Identifier Methods -- Embedding Information in an Identifier: Not Recommended -- One-Way Hashes -- Use Case: Hospital Registration -- Deidentification -- Data Scrubbing -- Reidentification -- Lessons Learned -- Chapter 3: Ontologies and Semantics -- Background -- Classifications, the Simplest of Ontologies -- Ontologies, Classes with Multiple Parents -- Choosing a Class Model -- Introduction to Resource Description Framework Schema -- Common Pitfalls in Ontology Development -- Chapter 4: Introspection -- Background -- Knowledge of Self -- eXtensible Markup Language -- Introduction to Meaning -- Namespaces and the Aggregation of Meaningful Assertions -- Resource Description Framework Triples -- Reflection -- Use Case: Trusted Time Stamp -- Summary -- Chapter 5: Data Integration and Software Interoperability -- Background -- The Committee to Survey Standards -- Standard Trajectory -- Specifications and Standards -- Versioning -- Compliance Issues -- Interfaces to Big Data Resources -- Chapter 6: Immutability and Immortality -- Background -- Immutability and Identifiers -- Data Objects -- Legacy Data -- Data Born from Data.

Reconciling Identifiers across Institutions -- Zero-Knowledge Reconciliation -- The Curatorś Burden -- Chapter 7: Measurement -- Background -- Counting -- Gene Counting -- Dealing with Negations -- Understanding Your Control -- Practical Significance of Measurements -- Obsessive-Compulsive Disorder: The Mark of a Great Data Manager -- Chapter 8: Simple but Powerful Big Data Techniques -- Background -- Look At the Data -- Data Range -- Denominator -- Frequency Distributions -- Mean and Standard Deviation -- Estimation-Only Analyses -- Use Case: Watching Data Trends with Google Ngrams -- Use Case: Estimating Movie Preferences -- Chapter 9: Analysis -- Background -- Analytic Tasks -- Clustering, Classifying, Recommending, and Modeling -- Clustering Algorithms -- Classifier Algorithms -- Recommender Algorithms -- Modeling Algorithms -- Data Reduction -- Normalizing and Adjusting Data -- Big Data Software: Speed and Scalability -- Find Relationships, Not Similarities -- Chapter 10: Special Considerations in Big Data Analysis -- Background -- Theory in Search of Data -- Data in Search of a Theory -- Overfitting -- Bigness Bias -- Too Much Data -- Fixing Data -- Data Subsets in Big Data: Neither Additive nor Transitive -- Additional Big Data Pitfalls -- Chapter 11: Stepwise Approach to Big Data Analysis -- Background -- Step 1. A Question Is Formulated -- Step 2. Resource Evaluation -- Step 3. A Question Is Reformulated -- Step 4. Query Output Adequacy -- Step 5. Data Description -- Step 6. Data Reduction -- Step 7. Algorithms Are Selected, if Absolutely Necessary -- Step 8. Results Are Reviewed and Conclusions Are Asserted -- Step 9. Conclusions Are Examined and Subjected to Validation -- Chapter 12: Failure -- Background -- Failure Is Common -- Failed Standards -- Complexity -- When Does Complexity Help? -- When Redundancy Fails -- Save Money.

Dont́ Protect Harmless Information -- After Failure -- Use Case: Cancer Biomedical Informatics Grid, a Bridge too Far -- Chapter 13: Legalities -- Background -- Responsibility for the Accuracy and Legitimacy of Contained Data -- Rights to Create, Use, and Share the Resource -- Copyright and Patent Infringements Incurred by Using Standards -- Protections for Individuals -- Consent -- Unconsented Data -- Good Policies Are a Good Policy -- Use Case: The Havasupai Story -- Chapter 14: Societal Issues -- Background -- How Big Data Is Perceived -- The Necessity of Data Sharing, Even When It Seems Irrelevant -- Reducing Costs and Increasing Productivity with Big Data -- Public Mistrust -- Saving Us from Ourselves -- Hubris and Hyperbole -- Chapter 15: The Future -- Background -- Will Big Data, Being Computationally Complex, Require a New Generation of Supercomputers? -- Will Big Data Achieve a Level of Complexity That Exceeds Our Ability to Fully Understand or Trust? -- Will We Need Armies of Computer Scientists Trained with the Most Advanced Techniques in Supercomputing? -- Will Big Data Create New Categories of Data Professionals for Which There Are Currently No Training Programs? -- Will Standardized Methods for Data Representation Be Uniformly Adopted, Thus Supporting Data Integration and Software Inter ... -- Will Big Data Be Accessible to the Public? -- Will Big Data Do More Harm Than Good? -- Can We Expect That Big Data Catastrophes Will Disrupt Vital Services, Cripple National Economies, and Destabilize World Pol ... -- Will Big Data Provide Answers to Important Questions That Could Not Otherwise Be Solved? -- Last Words -- Glossary -- References -- Index.