Introduction So What Is Big Data? Growing Interest in Decision Making What This Book Addresses The Conversation about Big Data Technological Change as a Driver of Big Data The Central Question: So What? Our Goals as Authors References The Mother of Invention''s Triplets: Moore''s Law, the Proliferation of Data, and Data Storage Technology Moore''s Law Parallel Computing, Between and Within Machines Quantum Computing Recap of Growth in Computing Power Storage, Storage Everywhere Grist for the Mill: Data Used and Unused Agriculture Automotive Marketing in the Physical World Online Marketing Asset Reliability and Efficiency Process Tracking and Automation Toward a Definition of Big Data Putting Big Data in Context Key Concepts of Big Data and Their Consequences Summary References. Hadoop Power through Distribution      Cost Effectiveness of Hadoop Not Every Problem Is a Nail      Some Technical Aspects Troubleshooting Hadoop Running Hadoop Hadoop File System      MapReduce Pig and Hive Installation Current Hadoop Ecosystem Hadoop Vendors      Cloudera Amazon Web Services (AWS) Hortonworks IBM Intel MapR Microsoft      To Run Pig Latin Using Powershell Pivotal References HBase and Other Big Data Databases Evolution from Flat File to the Three V'' s      Flat File      Hierarchical Database      Network Database      Relational Database      Object-Oriented Databases      Relational-Object Databases Transition to Big Data Databases      What Is Different bbout HBase?      What Is Bigtable?      What Is MapReduce?      What Are the Various Modalities for Big Data Databases? Graph Databases      How Does a Graph Database Work?      What is the Performance of a Graph Database? Document Databases Key-Value Databases Column-Oriented Databases      HBase      Apache Accumulo References Machine Learning Machine Learning Basics Classifying with Nearest Neighbors Naive Bayes Support Vector Machines Improving Classification with Adaptive Boosting Regression Logistic Regression Tree-Based Regression K-Means Clustering Apriori Algorithm Frequent Pattern-Growth Principal Component Analysis (PCA) Singular Value Decomposition Neural Networks Big Data and MapReduce Data Exploration Spam Filtering Ranking Predictive Regression Text Regression Multidimensional Scaling Social Graphing References Statistics Statistics, Statistics Everywhere Digging into the Data Standard Deviation: The Standard Measure of Dispersion The Power of Shapes: Distributions Distributions: Gaussian Curve Distributions: Why Be Normal? Distributions: The Long Arm of the Power Law The Upshot? Statistics Are not Bloodless Fooling Ourselves: Seeing What We Want to See in the Data We Can Learn Much from an Octopus Hypothesis Testing: Seeking a Verdict      Two-Tailed Testing Hypothesis Testing: A Broad Field Moving on to Specific Hypothesis Tests Regression and Correlation p Value in Hypothesis Testing: A Successful Gatekeeper? Specious Correlations and Overfitting the Data A Sample of Common Statistical Software Packages      Minitab      SPSS      R      SAS           Big Data Analytics           Hadoop Integration      Angoss      Statistica           Capabilities Summary References Google Big Data Giants Google      Go      Android      Google Product Offerings      Google Analytics           Advertising and Campaign Performance           Analysis and Testing Facebook Ning Non-United States Social Media      Tencent      Line      Sina Weibo      Odnoklassniki      Vkontakte      Nimbuzz Ranking Network Sites Negative Issues with Social Networks Amazon Some Final Words References Geographic Information Systems (GIS) GIS Implementations A GIS Example GIS Tools GIS Databases References Discovery Faceted Search versus Strict Taxonomy First Key Ability: Breaking Down Barriers Second Key Ability: Flexible Search and Navigation Underlying Technology The Upshot Summary References Data Quality Know Thy Data and Thyself Structured, Unstructured, and Semistructured Data Data Inconsistency: An Example from This Book The Black Swan and Incomplete Data How Data Can Fool Us      Ambiguous Data      Aging of Data or Variables      Missing Variables May Change the Meaning      Inconsistent Use of Units and Terminology Biases      Sampling Bias      Publication Bias      Survivorship Bias Data as a Video, Not a Snapshot: Different Viewpoints as a Noise Filter What Is My Toolkit for Improving My Data?      Ishikawa Diagram      Interrelationship Digraph      Force Field Analysis Data-Centric Methods      Troubleshooting Queries from Source Data      Troubleshooting Data Quality beyond the Source System      Using Our Hidden Resources Summary References Benefits Data Serendipity Converting Data Dreck to Usefulness Sales Returned Merchandise Security Medical Travel      Lodging      Vehicle      Meals Geographical Information Systems      New York City      Chicago CLEARMAP      Baltimore      San Francisco      Los Angeles      Tucson, Arizona, University of Arizona, and COPLINK Social Networking Education      General Educational Data      Legacy Data      Grades and other Indicators      Testing Results      Addresses, Phone Numbers, and More Concluding Comments References Concerns Part Two: Basic Principles of National Application      Collection Limitation Principle      Data Quality Principle      Purpose Specification Principle      Use Limitation Principle      Security Safeguards Principle      Openness Principle      Individual Participation Principle      Accountability Principle Logical Fallacies      Affirming the Consequent      Denying the Antecedent      Ludic Fallacy Cognitive Biases      Confirmation Bias      Notational Bias      Selection/Sample Bias      Halo Effect      Consistency and Hindsight Biases      Congruence Bias      Von Restorff Effect Data Serendipity      Converting Data Dreck to Usefulness Sales Merchandise Returns Security      CompStat      Medical Travel      Lodging      Vehicle      Meals Social Networking Education Making Yourself Harder to Track      Misinformation      Disinformation      Reducing/Eliminating Profiles           Social Media           Self Redefinition           Identi.