Cognitive Computing and Big Data Analytics -- Contents -- About the Technical Editors -- About the Authors -- Acknowledgments -- Introduction -- Chapter 1 The Foundation of Cognitive Computing -- Cognitive Computing as a New Generation -- The Uses of Cognitive Systems -- What Makes a System Cognitive? -- Gaining Insights from Data -- Domains Where Cognitive Computing Is Well Suited -- Artificial Intelligence as the Foundation of Cognitive Computing -- Understanding Cognition -- Two Systems of Judgment and Choice -- System 1-Automatic Thinking: Intuition and Biases -- System 2-Controlled, Rule-Centric, and Concentrated Effort -- Understanding Complex Relationships Between Systems -- Types of Adaptive Systems -- The Elements of a Cognitive System -- Infrastructure and Deployment Modalities -- Data Access, Metadata, and Management Services -- The Corpus, Taxonomies, and Data Catalogs -- Data Analytics Services -- Continuous Machine Learning -- Hypothesis Generation and Evaluation -- The Learning Process -- Presentation and Visualization Services -- Cognitive Applications -- Summary -- Chapter 2 Design Principles for Cognitive Systems -- Components of a Cognitive System -- Building the Corpus -- Corpus Management Regulatory and Security Considerations -- Bringing Data into the Cognitive System -- Leveraging Internal and External Data Sources -- Data Access and Feature Extraction Services -- Analytics Services -- Machine Learning -- Finding Patterns in Data -- Supervised Learning -- Reinforcement Learning -- Unsupervised Learning -- Hypotheses Generation and Scoring -- Hypothesis Generation -- Hypothesis Scoring -- Presentation and Visualization Services -- Infrastructure -- Summary -- Chapter 3 Natural Language Processing in Support of a Cognitive System -- The Role of NLP in a Cognitive System -- The Importance of Context.

Connecting Words for Meaning -- Understanding Linguistics -- Language Identification and Tokenization -- Phonology -- Morphology -- Lexical Analysis -- Syntax and Syntactic Analysis -- Construction Grammars -- Discourse Analysis -- Pragmatics -- Techniques for Resolving Structural Ambiguity -- Importance of Hidden Markov Models -- Word-Sense Disambiguation (WSD) -- Semantic Web -- Applying Natural Language Technologies to Business Problems -- Enhancing the Shopping Experience -- Leveraging the Connected World of Internet of Things -- Voice of the Customer -- Fraud Detection -- Summary -- Chapter 4 The Relationship Between Big Data and Cognitive Computing -- Dealing with Human-Generated Data -- Defining Big Data -- Volume, Variety, Velocity, and Veracity -- The Architectural Foundation for Big Data -- The Physical Foundation for Big Data -- Security Infrastructure -- Operational Databases -- Role of Structured and Unstructured Data -- Data Services and Tools -- Analytical Data Warehouses -- Big Data Analytics -- Hadoop -- Data in Motion and Streaming Data -- Analyzing Dark Data -- Integration of Big Data with Traditional Data -- Summary -- Chapter 5 Representing Knowledge in Taxonomies and Ontologies -- Representing Knowledge -- Developing a Cognitive System -- Defining Taxonomies and Ontologies -- Explaining How to Represent Knowledge -- Managing Multiple Views of Knowledge -- Models for Knowledge Representation -- Taxonomies -- Ontologies -- Other Methods of Knowledge Representation -- Simple Trees -- The Semantic Web -- The Importance of Persistence and State -- Implementation Considerations -- Summary -- Chapter 6 Applying Advanced Analytics to Cognitive Computing -- Advanced Analytics Is on a Path to Cognitive Computing -- Key Capabilities in Advanced Analytics -- The Relationship Between Statistics, Data Mining, and Machine Learning.

Using Machine Learning in the Analytics Process -- Supervised Learning -- Unsupervised Learning -- Predictive Analytics -- Business Value of Predictive Analytics -- Text Analytics -- Business Value of Text Analytics -- Image Analytics -- Speech Analytics -- Using Advanced Analytics to Create Value -- Building Value with In-memory Capabilities -- Impact of Open Source Tools on Advanced Analytics -- Summary -- Chapter 7 The Role of Cloud and Distributed Computing in Cognitive Computing -- Leveraging Distributed Computing for Shared Resources -- Why Cloud Services Are Fundamental to Cognitive Computing Systems -- Characteristics of Cloud Computing -- Elasticity and Self-service Provisioning -- Scaling -- Distributed Processing -- Cloud Computing Models -- The Public Cloud -- The Private Cloud -- Managed Service Providers -- The Hybrid Cloud Model -- Delivery Models of the Cloud -- Infrastructure as a Service -- Virtualization -- Software-defined Environment -- Containers -- Software as a Service -- Platform as a Service -- Managing Workloads -- Security and Governance -- Data Integration and Management in the Cloud -- Summary -- Chapter 8 The Business Implications of Cognitive Computing -- Preparing for Change -- Advantages of New Disruptive Models -- What Does Knowledge Mean to the Business? -- The Difference with a Cognitive Systems Approach -- Meshing Data Together Differently -- Using Business Knowledge to Plan for the Future -- Answering Business Questions in New Ways -- Building Business Specific Solutions -- Making Cognitive Computing a Reality -- How a Cognitive Application Can Change a Market -- Summary -- Chapter 9 IBM's Watson as a Cognitive System -- Watson Defined -- How Watson Is Different from Other Search Engines -- Advancing Research with a "Grand Challenge" -- Preparing Watson for Jeopardy!.

Preparing Watson for Commercial Applications -- Watson's Software Architecture -- The Components of DeepQA Architecture -- Building the Watson Corpus: Answer and Evidence Sources -- Source Acquisition -- Source Transformation -- Source Expansion and Updates -- Question Analysis -- Slot Grammar Parser and Components for Semantic Analysis -- Question Classification -- Hypothesis Generation -- Scoring and Confidence Estimation -- Summary -- Chapter 10 The Process of Building a Cognitive Application -- The Emerging Cognitive Platform -- Defining the Objective -- Defining the Domain -- Understanding the Intended Users and Defining their Attributes -- Defining Questions and Exploring Insights -- Typical Question-Answer Pairs -- Anticipatory Analytics -- Acquiring the Relevant Data Sources -- The Importance of Leveraging Structured Data Sources -- Analyzing Dark Data -- Leveraging External Data -- Creating and Refining the Corpora -- Preparing the Data -- Ingesting the Data -- Refining and Expanding the Corpora -- Governance of Data -- Training and Testing -- Summary -- Chapter 11 Building a Cognitive Healthcare Application -- Foundations of Cognitive Computing for Healthcare -- Constituents in the Healthcare Ecosystem -- Learning from Patterns in Healthcare Data -- Building on a Foundation of Big Data Analytics -- Cognitive Applications across the Healthcare Ecosystem -- Two Different Approaches to Emerging Cognitive Healthcare Applications -- The Role of Healthcare Ontologies in a Cognitive Application -- Starting with a Cognitive Application for Healthcare -- Define the Questions Users will Ask -- Ingest Content to Create the Corpus -- Training the Cognitive System -- Question Enrichment and Adding to the Corpus -- Using Cognitive Applications to Improve Health and Wellness -- Welltok -- Overview of Welltok's Solution -- CaféWell Concierge in Action.

GenieMD -- Consumer Health Data Platforms -- Using a Cognitive Application to Enhance the Electronic Medical Record -- Using a Cognitive Application to Improve Clinical Teaching -- Summary -- Chapter 12 Smarter Cities: Cognitive Computing in Government -- How Cities Have Operated -- The Characteristics of a Smart City -- Collecting Data for Planning -- Managing Operations -- Managing Security and Threats -- Managing Citizen-produced Documentation and Data -- Data Integration Across Government Departments -- The Rise of the Open Data Movement Will Fuel Cognitive Cities -- The Internet of Everything and Smarter Cities -- Understanding the Ownership and Value of Data -- Cities Are Adopting Smarter Technology Today for Major Functions -- Managing Law Enforcement Issues Cognitively -- The Problem of Correlating Crime Data -- The COPLink Project -- Smart Energy Management: From Visualization to Distribution -- The Problem of Integrating Regional Utilities Management -- The Area Energy Management Solutions Project -- The Cognitive Computing Opportunity -- Protecting the Power Grid with Machine Learning -- The Problem of Identifying Threats from New Patterns -- The Grid Cybersecurity Analytics Project -- The Cognitive Computing Opportunity -- Improving Public Health with Cognitive Community Services -- Smarter Approaches to Preventative Healthcare -- The Town Health Station Project -- The Cognitive Computing Opportunity -- Building a Smarter Transportation Infrastructure -- Managing Traffic in Growing Cities -- The Adaptive Traffic Signals Controller Project -- The Cognitive Computing Opportunity -- Using Analytics to Close the Workforce Skills Gap -- Identifying Emerging Skills Requirements and Just-in-Time Training -- The Digital On-Ramps (DOR) Project -- The Cognitive Computing Opportunity -- Creating a Cognitive Community Infrastructure.

The Smart + Connected Communities Initiative.