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Other Books by Jules J. Berman
by Jules J. Berman
Principles and Practice of Big Data, 2nd Edition
Cover image
Title page
Table of Contents
Copyright
Other Books by Jules J. Berman
Dedication
About the Author
Author's Preface to Second Edition
Abstract
Author's Preface to First Edition
1: Introduction
Abstract
Section 1.1. Definition of Big Data
Section 1.2. Big Data Versus Small Data
Section 1.3. Whence Comest Big Data?
Section 1.4. The Most Common Purpose of Big Data Is to Produce Small Data
Section 1.5. Big Data Sits at the Center of the Research Universe
2: Providing Structure to Unstructured Data
Abstract
Section 2.1. Nearly All Data Is Unstructured and Unusable in Its Raw Form
Section 2.2. Concordances
Section 2.3. Term Extraction
Section 2.4. Indexing
Section 2.5. Autocoding
Section 2.6. Case Study: Instantly Finding the Precise Location of Any Atom in the Universe (Some Assembly Required)
Section 2.7. Case Study (Advanced): A Complete Autocoder (in 12 Lines of Python Code)
Section 2.8. Case Study: Concordances as Transformations of Text
Section 2.9. Case Study (Advanced): Burrows Wheeler Transform (BWT)
3: Identification, Deidentification, and Reidentification
Abstract
Section 3.1. What Are Identifiers?
Section 3.2. Difference Between an Identifier and an Identifier System
Section 3.3. Generating Unique Identifiers
Section 3.4. Really Bad Identifier Methods
Section 3.5. Registering Unique Object Identifiers
Section 3.6. Deidentification and Reidentification
Section 3.7. Case Study: Data Scrubbing
Section 3.8. Case Study (Advanced): Identifiers in Image Headers
Section 3.9. Case Study: One-Way Hashes
4: Metadata, Semantics, and Triples
Abstract
Section 4.1. Metadata
Section 4.2. eXtensible Markup Language
Section 4.3. Semantics and Triples
Section 4.4. Namespaces
Section 4.5. Case Study: A Syntax for Triples
Section 4.6. Case Study: Dublin Core
5: Classifications and Ontologies
Abstract
Section 5.1. It's All About Object Relationships
Section 5.2. Classifications, the Simplest of Ontologies
Section 5.3. Ontologies, Classes With Multiple Parents
Section 5.4. Choosing a Class Model
Section 5.5. Class Blending
Section 5.6. Common Pitfalls in Ontology Development
Section 5.7. Case Study: An Upper Level Ontology
Section 5.8. Case Study (Advanced): Paradoxes
Section 5.9. Case Study (Advanced): RDF Schemas and Class Properties
Section 5.10. Case Study (Advanced): Visualizing Class Relationships
6: Introspection
Abstract
Section 6.1. Knowledge of Self
Section 6.2. Data Objects: The Essential Ingredient of Every Big Data Collection
Section 6.3. How Big Data Uses Introspection
Section 6.4. Case Study: Time Stamping Data
Section 6.5. Case Study: A Visit to the TripleStore
Section 6.6. Case Study (Advanced): Proof That Big Data Must Be Object-Oriented
7: Standards and Data Integration
Abstract
Section 7.1. Standards
Section 7.2. Specifications Versus Standards
Section 7.3. Versioning
Section 7.4. Compliance Issues
Section 7.5. Case Study: Standardizing the Chocolate Teapot
8: Immutability and Immortality
Abstract
Section 8.1. The Importance of Data That Cannot Change
Section 8.2. Immutability and Identifiers
Section 8.3. Coping With the Data That Data Creates
Section 8.4. Reconciling Identifiers Across Institutions
Section 8.5. Case Study: The Trusted Timestamp
Section 8.6. Case Study: Blockchains and Distributed Ledgers
Section 8.7. Case Study (Advanced): Zero-Knowledge Reconciliation
9: Assessing the Adequacy of a Big Data Resource
Abstract
Section 9.1. Looking at the Data
Section 9.2. The Minimal Necessary Properties of Big Data
Section 9.3. Data That Comes With Conditions
Section 9.4. Case Study: Utilities for Viewing and Searching Large Files
Section 9.5. Case Study: Flattened Data
10: Measurement
Abstract
Section 10.1. Accuracy and Precision
Section 10.2. Data Range
Section 10.3. Counting
Section 10.4. Normalizing and Transforming Your Data
Section 10.5. Reducing Your Data
Section 10.6. Understanding Your Control
Section 10.7. Statistical Significance Without Practical Significance
Section 10.8. Case Study: Gene Counting
Section 10.9. Case Study: Early Biometrics, and the Significance of Narrow Data Ranges
11: Indispensable Tips for Fast and Simple Big Data Analysis
Abstract
Section 11.1. Speed and Scalability
Section 11.2. Fast Operations, Suitable for Big Data, That Every Computer Supports
Section 11.3. The Dot Product, a Simple and Fast Correlation Method
Section 11.4. Clustering
Section 11.5. Methods for Data Persistence (Without Using a Database)
Section 11.6. Case Study: Climbing a Classification
Section 11.7. Case Study (Advanced): A Database Example
Section 11.8. Case Study (Advanced): NoSQL
12: Finding the Clues in Large Collections of Data
Abstract
Section 12.1. Denominators
Section 12.2. Word Frequency Distributions
Section 12.3. Outliers and Anomalies
Section 12.4. Back-of-Envelope Analyses
Section 12.5. Case Study: Predicting User Preferences
Section 12.6. Case Study: Multimodality in Population Data
Section 12.7. Case Study: Big and Small Black Holes
13: Using Random Numbers to Knock Your Big Data Analytic Problems Down to Size
Abstract
Section 13.1. The Remarkable Utility of (Pseudo)Random Numbers
Section 13.2. Repeated Sampling
Section 13.3. Monte Carlo Simulations
Section 13.4. Case Study: Proving the Central Limit Theorem
Section 13.5. Case Study: Frequency of Unlikely String of Occurrences
Section 13.6. Case Study: The Infamous Birthday Problem
Section 13.7. Case Study (Advanced): The Monty Hall Problem
Section 13.8. Case Study (Advanced): A Bayesian Analysis
14: Special Considerations in Big Data Analysis
Abstract
Section 14.1. Theory in Search of Data
Section 14.2. Data in Search of Theory
Section 14.3. Bigness Biases
Section 14.4. Data Subsets in Big Data: Neither Additive Nor Transitive
Section 14.5. Additional Big Data Pitfalls
Section 14.6. Case Study (Advanced): Curse of Dimensionality
15: Big Data Failures and How to Avoid (Some of) Them
Abstract
Section 15.1. Failure Is Common
Section 15.2. Failed Standards
Section 15.3. Blaming Complexity
Section 15.4. An Approach to Big Data That May Work for You
Section 15.5. After Failure
Section 15.6. Case Study: Cancer Biomedical Informatics Grid, a Bridge Too Far
Section 15.7. Case Study: The Gaussian Copula Function
16: Data Reanalysis: Much More Important Than Analysis
Abstract
Section 16.1. First Analysis (Nearly) Always Wrong
Section 16.2. Why Reanalysis Is More Important Than Analysis
Section 16.3. Case Study: Reanalysis of Old JADE Collider Data
Section 16.4. Case Study: Vindication Through Reanalysis
Section 16.5. Case Study: Finding New Planets From Old Data
17: Repurposing Big Data
Abstract
Section 17.1. What Is Data Repurposing?
Section 17.2. Dark Data, Abandoned Data, and Legacy Data
Section 17.3. Case Study: From Postal Code to Demographic Keystone
Section 17.4. Case Study: Scientific Inferencing From a Database of Genetic Sequences
Section 17.5. Case Study: Linking Global Warming to High-Intensity Hurricanes
Section 17.6. Case Study: Inferring Climate Trends With Geologic Data
Section 17.7. Case Study: Lunar Orbiter Image Recovery Project
18: Data Sharing and Data Security
Abstract
Section 18.1. What Is Data Sharing, and Why Don't We Do More of It?
Section 18.2. Common Complaints
Section 18.3. Data Security and Cryptographic Protocols
Section 18.4. Case Study: Life on Mars
Section 18.5. Case Study: Personal Identifiers
19: Legalities
Abstract
Section 19.1. Responsibility for the Accuracy and Legitimacy of Data
Section 19.2. Rights to Create, Use, and Share the Resource
Section 19.3. Copyright and Patent Infringements Incurred by Using Standards
Section 19.4. Protections for Individuals
Section 19.5. Consent
Section 19.6. Unconsented Data
Section 19.7. Privacy Policies
Section 19.8. Case Study: Timely Access to Big Data
Section 19.9. Case Study: The Havasupai Story
20: Societal Issues
Abstract
Section 20.1. How Big Data Is Perceived by the Public
Section 20.2. Reducing Costs and Increasing Productivity With Big Data
Section 20.3. Public Mistrust
Section 20.4. Saving Us From Ourselves
Section 20.5. Who Is Big Data?
Section 20.6. Hubris and Hyperbole
Section 20.7. Case Study: The Citizen Scientists
Section 20.8. Case Study: 1984, by George Orwell
Index
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