Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support (11th Edition)

Front Cover
Title Page
Copyright Page
Brief Contents
Contents
Preface
About the Authors
Part I Introduction to Analytics and AI
	Chapter 1 Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support
		1.1 Opening Vignette: How Intelligent Systems Work for KONE Elevators and Escalators Company
		1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics
			Decision-Making Process
			The Influence of the External and Internal Environments on the Process
			Data and Its Analysis in Decision Making
			Technologies for Data Analysis and Decision Support
		1.3 Decision-Making Processes and Computerized Decision Support Framework
			Simon’s Process: Intelligence, Design, and Choice
			The Intelligence Phase: Problem (or Opportunity) Identification
		Application Case 1.1 Making Elevators Go Faster!
			The Design Phase
			The Choice Phase
			The Implementation Phase
			The Classical Decision Support System Framework
			A DSS Application
			Components of a Decision Support System
			The Data Management Subsystem
			The Model Management Subsystem
		Application Case 1.2 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions
			The User Interface Subsystem
			The Knowledge-Based Management Subsystem
		1.4 Evolution of Computerized Decision Support to Business Intelligence/Analytics/Data Science
			A Framework for Business Intelligence
			The Architecture of BI
			The Origins and Drivers of BI
			Data Warehouse as a Foundation for Business Intelligence
			Transaction Processing versus Analytic Processing
			A Multimedia Exercise in Business Intelligence
		1.5 Analytics Overview
		Application Case 1.3 Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities
		Application Case 1.4 Siemens Reduces Cost with the Use of Data Visualization
		Application Case 1.5 Analyzing Athletic Injuries
		Application Case 1.6 A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates
		1.6 Analytics Examples in Selected Domains
		Application Case 1.7 Image Analysis Helps Estimate Plant Cover
		1.7 Artificial Intelligence Overview
			What Is Artificial Intelligence?
			The Major Benefits of AI
			The Landscape of AI
		Application Case 1.8 AI Increases Passengers’ Comfort and Security in Airports and Borders
			The Three Flavors of AI Decisions
			Autonomous AI
			Societal Impacts
		Application Case 1.9 Robots Took the Job of Camel-Racing Jockeys for Societal Benefits
		1.8 Convergence of Analytics and AI
			Major Differences between Analytics and AI
			Why Combine Intelligent Systems?
			How Convergence Can Help?
			Big Data Is Empowering AI Technologies
			The Convergence of AI and the IoT
			The Convergence with Blockchain and Other Technologies
		Application Case 1.10 Amazon Go Is Open for Business
			IBM and Microsoft Support for Intelligent Systems Convergence
		1.9 Overview of the Analytics Ecosystem
		1.10 Plan of the Book
		1.11 Resources, Links, and the Teradata University Network Connection
			Resources and Links
			Vendors, Products, and Demos
			Periodicals
			The Teradata University Network Connection
			The Book’s Web Site
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		References
	Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications
		2.1 Opening Vignette: Inrix Solves Transportation Problems
		2.2 Introduction to Artificial Intelligence
			Definitions
			Major Characteristics of AI Machines
			Major Elements of AI
			AI Applications
			Major Goals of AI
			Drivers of AI
			Benefits of AI
			Some Limitations of AI Machines
			Three Flavors of AI Decisions
			Artificial Brain
		2.3 Human and Computer Intelligence
			What Is Intelligence?
			How Intelligent Is AI?
			Measuring AI
		0 Application Case 2.1 How Smart Can a Vacuum Cleaner Be?
		2.4 Major AI Technologies and Some Derivatives
			Intelligent Agents
			Machine Learning
		0 Application Case 2.2 How Machine Learning Is Improving Work in Business
			Machine and Computer Vision
			Robotic Systems
			Natural Language Processing
			Knowledge and Expert Systems and Recommenders
			Chatbots
			Emerging AI Technologies
		2.5 AI Support for Decision Making
			Some Issues and Factors in Using AI in Decision Making
			AI Support of the Decision-Making Process
			Automated Decision Making
		0 Application Case 2.3 How Companies Solve Real-World Problems Using Google’s Machine-Learning Tools
			Conclusion
		2.6 AI Applications in Accounting
			AI in Accounting: An Overview
			AI in Big Accounting Companies
			Accounting Applications in Small Firms
		0 Application Case 2.4 How EY, Deloitte, and PwC Are Using AI
			Job of Accountants
		2.7 AI Applications in Financial Services
			AI Activities in Financial Services
			AI in Banking: An Overview
			Illustrative AI Applications in Banking
			Insurance Services
		0 Application Case 2.5 US Bank Customer Recognition and Services
		2.8 AI in Human Resource Management (HRM)
			AI in HRM: An Overview
			AI in Onboarding
		0 Application Case 2.6 How Alexander Mann Solutions (AMS) Is Using AI to Support the Recruiting Process
			Introducing AI to HRM Operations
		2.9 AI in Marketing, Advertising, and CRM
			Overview of Major Applications
			AI Marketing Assistants in Action
			Customer Experiences and CRM
		0 Application Case 2.7 Kraft Foods Uses AI for Marketing and CRM
			Other Uses of AI in Marketing
		2.10 AI Applications in Production-Operation Management (POM)
			AI in Manufacturing
			Implementation Model
			Intelligent Factories
			Logistics and Transportation
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		References
	Chapter 3 Nature of Data, Statistical Modeling, and Visualization
		3.1 Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing
		3.2 Nature of Data
		3.3 Simple Taxonomy of Data
		0 Application Case 3.1 Verizon Answers the Call for Innovation: The Nation’s Largest Network Provider uses Advanced Analytics to Bring the Future to its Customers
		3.4 Art and Science of Data Preprocessing
		0 Application Case 3.2 Improving Student Retention with Data-Driven Analytics
		3.5 Statistical Modeling for Business Analytics
			Descriptive Statistics for Descriptive Analytics
			Measures of Centrality Tendency (Also Called Measures of Location or Centrality)
			Arithmetic Mean
			Median
			Mode
			Measures of Dispersion (Also Called Measures of Spread or Decentrality)
			Range
			Variance
			Standard Deviation
			Mean Absolute Deviation
			Quartiles and Interquartile Range
			Box-and-Whiskers Plot
			Shape of a Distribution
		0 Application Case 3.3 Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and Detect Problems
		3.6 Regression Modeling for Inferential Statistics
			How Do We Develop the Linear Regression Model?
			How Do We Know If the Model Is Good Enough?
			What Are the Most Important Assumptions in Linear Regression?
			Logistic Regression
			Time-Series Forecasting
		0 Application Case 3.4 Predicting NCAA Bowl Game Outcomes
		3.7 Business Reporting
		0 Application Case 3.5 Flood of Paper Ends at Fema
		3.8 Data Visualization
			Brief History of Data Visualization
		0 Application Case 3.6 Macfarlan Smith Improves Operational Performance Insight with Tableau Online
		3.9 Different Types of Charts and Graphs
			Basic Charts and Graphs
			Specialized Charts and Graphs
			Which Chart or Graph Should You Use?
		3.10 Emergence of Visual Analytics
			Visual Analytics
			High-Powered Visual Analytics Environments
		3.11 Information Dashboards
		0 Application Case 3.7 Dallas Cowboys Score Big with Tableau and Teknion
			Dashboard Design
		0 Application Case 3.8 Visual Analytics Helps Energy Supplier Make Better Connections
			What to Look for in a Dashboard
			Best Practices in Dashboard Design
			Benchmark Key Performance Indicators with Industry Standards
			Wrap the Dashboard Metrics with Contextual Metadata
			Validate the Dashboard Design by a Usability Specialist
			Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard
			Enrich the Dashboard with Business-User Comments
			Present Information in Three Different Levels
			Pick the Right Visual Construct Using Dashboard Design Principles
			Provide for Guided Analytics
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		References
Part II Predictive Analytics/Machine Learning
	Chapter 4 Data Mining Process, Methods, and Algorithms
		4.1 Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime
		4.2 Data Mining Concepts
		0 Application Case 4.1 Visa Is Enhancing the Customer Experience while Reducing Fraud with Predictive Analytics and Data Mining
			Definitions, Characteristics, and Benefits
			How Data Mining Works
		4.3 Data Mining Applications
		0 Application Case 4.3 Predictive Analytic and Data Mining Help Stop Terrorist Funding
		4.4 Data Mining Process
			Step 1: Business Understanding
			Step 2: Data Understanding
			Step 3: Data Preparation
			Step 4: Model Building
		0 Application Case 4.4 Data Mining Helps in Cancer Research
			Step 5: Testing and Evaluation
			Step 6: Deployment
			Other Data Mining Standardized Processes and Methodologies
		4.5 Data Mining Methods
			Classification
			Estimating the True Accuracy of Classification Models
			Estimating the Relative Importance of Predictor Variables
			Cluster Analysis for Data Mining
		0 Application Case 4.5
			Association Rule Mining
		4.6 Data Mining Software Tools
		0 Application Case 4.6
		4.7 Data Mining Privacy Issues, Myths, and Blunders
		0 Application Case 4.7
			Data Mining Myths and Blunders
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		References
	Chapter 5 Machine-Learning Techniques for Predictive Analytics
		5.1 Opening Vignette: Predictive Modeling Helps Better Understand and Manage Complex Medical Procedures
		5.2 Basic Concepts of Neural Networks
			Biological versus Artificial Neural Networks
		0 Application Case 5.1 Neural Networks are Helping to Save Lives in the Mining Industry
		5.3 Neural Network Architectures
			Kohonen’s Self-Organizing Feature Maps
			Hopfield Networks
		0 Application Case 5.2 Predictive Modeling Is Powering the Power Generators
		5.4 Support Vector Machines
		0 Application Case 5.3 Identifying Injury Severity Risk Factors in Vehicle Crashes with Predictive Analytics
			Mathematical Formulation of SVM
			Primal Form
			Dual Form
			Soft Margin
			Nonlinear Classification
			Kernel Trick
		5.5 Process-Based Approach to the Use of SVM
			Support Vector Machines versus Artificial Neural Networks
		5.6 Nearest Neighbor Method for Prediction
			Similarity Measure: The Distance Metric
			Parameter Selection
		0 Application Case 5.4 Efficient Image Recognition and Categorization with knn
		5.7 Naïve Bayes Method for Classification
			Bayes Theorem
			Naïve Bayes Classifier
			Process of Developing a Naïve Bayes Classifier
			Testing Phase
		0 Application Case 5.5 Predicting Disease Progress in Crohn’s Disease Patients: A Comparison of Analytics Methods
		5.8 Bayesian Networks
			How Does BN Work?
			How Can BN Be Constructed?
		5.9 Ensemble Modeling
			Motivation—Why Do We Need to Use Ensembles?
			Different Types of Ensembles
			Bagging
			Boosting
			Variants of Bagging and Boosting
			Stacking
			Information Fusion
			Summary—Ensembles are not Perfect!
		0 Application Case 5.6 To Imprison or Not to Imprison: A Predictive Analytics-Based Decision Support System for Drug Courts
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		Internet Exercises
		References
	Chapter 6 Deep Learning and Cognitive Computing
		6.1 Opening Vignette: Fighting Fraud with Deep Learning and Artificial Intelligence
		6.2 Introduction to Deep Learning
		0 Application Case 6.1 Finding the Next Football Star with Artificial Intelligence
		6.3 Basics of “Shallow” Neural Networks
		0 Application Case 6.2 Gaming Companies Use Data Analytics to Score Points with Players
		0 Application Case 6.3 Artificial Intelligence Helps Protect Animals from Extinction
		6.4 Process of Developing Neural Network–Based Systems
			Learning Process in ANN
			Backpropagation for ANN Training
		6.5 Illuminating the Black Box of ANN
		0 Application Case 6.4 Sensitivity Analysis Reveals Injury Severity Factors in Traffic Accidents
		6.6 Deep Neural Networks
			Feedforward Multilayer Perceptron (MLP)-Type Deep Networks
			Impact of Random Weights in Deep MLP
			More Hidden Layers versus More Neurons?
		0 Application Case 6.5 Georgia DOT Variable Speed Limit Analytics Help Solve Traffic Congestions
		6.7 Convolutional Neural Networks
			Convolution Function
			Pooling
			Image Processing Using Convolutional Networks
		0 Application Case 6.6 From Image Recognition to Face Recognition
			Text Processing Using Convolutional Networks
		6.8 Recurrent Networks and Long Short-Term Memory Networks
		0 Application Case 6.7 Deliver Innovation by Understanding Customer Sentiments
			LSTM Networks Applications
		6.9 Computer Frameworks for Implementation of Deep Learning
			Torch
			Caffe
			TensorFlow
			Theano
			Keras: An Application Programming Interface
		6.10 Cognitive Computing
			How Does Cognitive Computing Work?
			How Does Cognitive Computing Differ from AI?
			Cognitive Search
			IBM Watson: Analytics at Its Best
		0 Application Case 6.8 IBM Watson Competes against the Best at Jeopardy!
			How Does Watson Do It?
			What Is the Future for Watson?
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		References
	Chapter 7 Text Mining, Sentiment Analysis, and Social Analytics
		7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into Near-Real-Time Sales
		7.2 Text Analytics and Text Mining Overview
		0 Application Case 7.1 Netflix: Using Big Data to Drive Big Engagement: Unlocking the Power of Analytics to Drive Content and Consumer Insight
		7.3 Natural Language Processing (NLP)
		0 Application Case 7.2 AMC Networks Is Using Analytics to Capture New Viewers, Predict Ratings, and Add Value for Advertisers in a Multichannel World
		7.4 Text Mining Applications
			Marketing Applications
			Security Applications
			Biomedical Applications
		0 Application Case 7.3 Mining for Lies
			Academic Applications
		0 Application Case 7.4 The Magic Behind the Magic: Instant Access to Information Helps the Orlando Magic Up their Game and the Fan’s Experience
		7.5 Text Mining Process
			Task 1: Establish the Corpus
			Task 2: Create the Term–Document Matrix
			Task 3: Extract the Knowledge
		0 Application Case 7.5 Research Literature Survey with Text Mining
		7.6 Sentiment Analysis
		0 Application Case 7.6 Creating a Unique Digital Experience to Capture Moments That Matter at Wimbledon
			Sentiment Analysis Applications
			Sentiment Analysis Process
			Methods for Polarity Identification
			Using a Lexicon
			Using a Collection of Training Documents
			Identifying Semantic Orientation of Sentences and Phrases
			Identifying Semantic Orientation of Documents
		7.7 Web Mining Overview
			Web Content and Web Structure Mining
		7.8 Search Engines
			Anatomy of a Search Engine
			1. Development Cycle
			2. Response Cycle
			Search Engine Optimization
			Methods for Search Engine Optimization
		0 Application Case 7.7 Delivering Individualized Content and Driving Digital Engagement: How Barbour Collected More Than 49,000 New Leads in One Month with Teradata Interactive
		7.9 Web Usage Mining (Web Analytics)
			Web Analytics Technologies
			Web Analytics Metrics
			Web Site Usability
			Traffic Sources
			Visitor Profiles
			Conversion Statistics
		7.10 Social Analytics
			Social Network Analysis
			Social Network Analysis Metrics
		0 Application Case 7.8 Tito’s Vodka Establishes Brand Loyalty with an Authentic Social Strategy
			Connections
			Distributions
			Segmentation
			Social Media Analytics
			How Do People Use Social Media?
			Measuring the Social Media Impact
			Best Practices in Social Media Analytics
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		References
Part III Prescriptive Analytics and Big Data
	Chapter 8 Prescriptive Analytics: Optimization and Simulation
		8.1 Opening Vignette: School District of Philadelphia Uses Prescriptive Analytics to Find Optimal Solution for Awarding Bus Route Contracts
		8.2 Model-Based Decision Making
		0 Application Case 8.1 Canadian Football League Optimizes Game Schedule
			Prescriptive Analytics Model Examples
			Identification of the Problem and Environmental Analysis
		0 Application Case 8.2 Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions
			Model Categories
		8.3 Structure of Mathematical Models for Decision Support
			The Components of Decision Support Mathematical Models
			The Structure of Mathematical Models
		8.4 Certainty, Uncertainty, and Risk
			Decision Making under Certainty
			Decision Making under Uncertainty
			Decision Making under Risk (Risk Analysis)
		0 Application Case 8.3 American Airlines Uses Should-Cost Modeling to Assess the Uncertainty of Bids for Shipment Routes
		8.5 Decision Modeling with Spreadsheets
		0 Application Case 8.4 Pennsylvania Adoption Exchange Uses Spreadsheet Model to Better Match Children with Families
		0 Application Case 8.5 Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Routes
		8.6 Mathematical Programming Optimization
		0 Application Case 8.6 Mixed-Integer Programming Model Helps the University of Tennessee Medical Center with Scheduling Physicians
			Linear Programming Model
			Modeling in LP: An Example
			Implementation
		8.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
			Multiple Goals
			Sensitivity Analysis
			What-If Analysis
			Goal Seeking
		8.8 Decision Analysis with Decision Tables and Decision Trees
			Decision Tables
			Decision Trees
		8.9 Introduction to Simulation
			Major Characteristics of Simulation
		0 Application Case 8.7 Steel Tubing Manufacturer Uses a Simulation-Based Production Scheduling System
			Advantages of Simulation
			Disadvantages of Simulation
			The Methodology of Simulation
			Simulation Types
			Monte Carlo Simulation
			Discrete Event Simulation
		0 Application Case 8.8 Cosan Improves Its Renewable Energy Supply Chain Using Simulation
		8.10 Visual Interactive Simulation
			Conventional Simulation Inadequacies
			Visual Interactive Simulation
			Visual Interactive Models and DSS
			Simulation Software
		0 Application Case 8.9 Improving Job-Shop Scheduling Decisions through RFID: A Simulation-Based Assessment
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		References
	Chapter 9 Big Data, Cloud Computing, and Location Analytics: Concepts and Tools
		9.1 Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data Methods
		9.2 Definition of Big Data
			The “V”s That Define Big Data
		0 Application Case 9.1 Alternative Data for Market Analysis or Forecasts
		9.3 Fundamentals of Big Data Analytics
			Business Problems Addressed by Big Data Analytics
		0 Application Case 9.2 Overstock.com Combines Multiple Datasets to Understand Customer Journeys
		9.4 Big Data Technologies
			MapReduce
			Why Use MapReduce?
			Hadoop
			How Does Hadoop Work?
			Hadoop Technical Components
			Hadoop: The Pros and Cons
			NoSQL
		0 Application Case 9.3 eBay’s Big Data Solution
		0 Application Case 9.4 Understanding Quality and Reliability of Healthcare Support Information on Twitter
		9.5 Big Data and Data Warehousing
			Use Cases for Hadoop
			Use Cases for Data Warehousing
			The Gray Areas (Any One of the Two Would Do the Job)
			Coexistence of Hadoop and Data Warehouse
		9.6 In-Memory Analytics and Apache Spark™
		0 Application Case 9.5 Using Natural Language Processing to analyze customer feedback in TripAdvisor reviews
			Architecture of Apache SparkTM
			Getting Started with Apache SparkTM
		9.7 Big Data and Stream Analytics
			Stream Analytics versus Perpetual Analytics
			Critical Event Processing
			Data Stream Mining
			Applications of Stream Analytics
			e-Commerce
			Telecommunications
		0 Application Case 9.6 Salesforce Is Using Streaming Data to Enhance Customer Value
			Law Enforcement and Cybersecurity
			Power Industry
			Financial Services
			Health Sciences
			Government
		9.8 Big Data Vendors and Platforms
			Infrastructure Services Providers
			Analytics Solution Providers
			Business Intelligence Providers Incorporating Big Data
		0 Application Case 9.7 Using Social Media for Nowcasting Flu Activity
		0 Application Case 9.8 Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse
		9.9 Cloud Computing and Business Analytics
			Data as a Service (DaaS)
			Software as a Service (SaaS)
			Platform as a Service (PaaS)
			Infrastructure as a Service (IaaS)
			Essential Technologies for Cloud Computing
		0 Application Case 9.9 Major West Coast Utility Uses Cloud-Mobile Technology to Provide Real-Time Incident Reporting
			Cloud Deployment Models
			Major Cloud Platform Providers in Analytics
			Analytics as a Service (AaaS)
			Representative Analytics as a Service Offerings
			Illustrative Analytics Applications Employing the Cloud Infrastructure
			Using Azure IOT, Stream Analytics, and Machine Learning to Improve Mobile Health Care Services
			Gulf Air Uses Big Data to Get Deeper Customer Insight
			Chime Enhances Customer Experience Using Snowflake
		9.10 Location-Based Analytics for Organizations
			Geospatial Analytics
		0 Application Case 9.10 Great Clips Employs Spatial Analytics to Shave Time in Location Decisions
		0 Application Case 9.11 Starbucks Exploits GIS and Analytics to Grow Worldwide
			Real-Time Location Intelligence
			Analytics Applications for Consumers
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		References
Part IV Robotics, Social Networks, AI and IoT
	Chapter 10 Robotics: Industrial and Consumer Applications
		10.1 Opening Vignette: Robots Provide Emotional Support to Patients and Children
		10.2 Overview of Robotics
		10.3 History of Robotics
		10.4 Illustrative Applications of Robotics
			Changing Precision Technology
			Adidas
			BMW Employs Collaborative Robots
			Tega
			San Francisco Burger Eatery
			Spyce
			Mahindra & Mahindra Ltd.
			Robots in the Defense Industry
			Pepper
			Da Vinci Surgical System
			Snoo – A Robotic Crib
			MEDi
			Care-E Robot
			Agrobot
		10.5 Components of Robots
		10.6 Various Categories of Robots
		10.7 Autonomous Cars: Robots in Motion
			Autonomous Vehicle Development
			Issues with Self-Driving Cars
		10.8 Impact of Robots on Current and Future Jobs
		10.9 Legal Implications of Robots and Artificial Intelligence
			Tort Liability
			Patents
			Property
			Taxation
			Practice of Law
			Constitutional Law
			Professional Certification
			Law Enforcement
		Chapter Highlights
		Key Terms
		Exercises
		References
	Chapter 11 Group Decision Making, Collaborative Systems, and AI Support
		11.2 Making Decisions in Groups: Characteristics, Process, Benefits, and Dysfunctions
			Characteristics of Group Work
			Types of Decisions Made by Groups
			Group Decision-Making Process
			Benefits and Limitations of Group Work
		11.3 Supporting Group Work and Team Collaboration with Computerized Systems
			Overview of Group Support Systems (GSS)
			Time/Place Framework
			Group Collaboration for Decision Support
		11.4 Electronic Support for Group Communication and Collaboration
			Groupware for Group Collaboration
			Synchronous versus Asynchronous Products
			Virtual Meeting Systems
			Collaborative Networks and Hubs
			Collaborative Hubs
			Social Collaboration
			Sample of Popular Collaboration Software
		11.5 Direct Computerized Support for Group Decision Making
			Group Decision Support Systems (GDSS)
			Characteristics of GDSS
			Supporting the Entire Decision-Making Process
			Brainstorming for Idea Generation and Problem Solving
			Group Support Systems
		11.6 Collective Intelligence and Collaborative Intelligence
			Definitions and Benefits
			Computerized Support to Collective Intelligence
		0 Application Case 11.1 Collaborative Modeling for Optimal Water Management: The Oregon State University Project
			How Collective Intelligence May Change Work and Life
			Collaborative Intelligence
			How to Create Business Value from Collaboration: The IBM Study
		11.7 Crowdsourcing as a Method for Decision Support
			The Essentials of Crowdsourcing
			Crowdsourcing for Problem-Solving and Decision Support
			Implementing Crowdsourcing for Problem Solving
		0 Application Case 11.2 How InnoCentive Helped GSK Solve a Difficult Problem
		11.8 Artificial Intelligence and Swarm AI Support of Team Collaboration and Group Decision Making
			AI Support of Group Decision Making
			AI Support of Team Collaboration
			Swarm Intelligence and Swarm AI
		0 Application Case 11.3 XPRIZE Optimizes Visioneering
		11.9 Human–Machine Collaboration and Teams of Robots
			Human–Machine Collaboration in Cognitive Jobs
			Robots as Coworkers: Opportunities and Challenges
			Teams of collaborating Robots
		Chapter Highlights
		Key Terms
		Exercises
		References
	Chapter 12 Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, and Robo Advisors
		12.1 Opening Vignette: Sephora Excels with Chatbots
		12.2 Expert Systems and Recommenders
			Basic Concepts of Expert Systems (ES)
			Characteristics and Benefits of ES
			Typical Areas for ES Applications
			Structure and Process of ES
		0 Application Case 12.1 ES Aid in Identification of Chemical, Biological, and Radiological Agents
			Why the Classical Type of ES Is Disappearing
		0 Application Case 12.2 VisiRule
			Recommendation Systems
		0 Application Case 12.3 Netflix Recommender: A Critical Success Factor
		12.3 Concepts, Drivers, and Benefits of Chatbots
			What Is a Chatbot?
			Chatbot Evolution
			Components of Chatbots and the Process of Their Use
			Drivers and Benefits
			Representative Chatbots from Around the World
		12.4 Enterprise Chatbots
			The Interest of Enterprises in Chatbots
			Enterprise Chatbots: Marketing and Customer Experience
		0 Application Case 12.4 WeChat’s Super Chatbot
		0 Application Case 12.5 How Vera Gold Mark Uses Chatbots to Increase Sales
			Enterprise Chatbots: Financial Services
			Enterprise Chatbots: Service Industries
		12.5 Virtual Personal Assistants
			Assistant for Information Search
			If You Were Mark Zuckerberg, Facebook CEO
			Amazon’s Alexa and Echo
			Apple’s Siri
			Google Assistant
			Other Personal Assistants
			Competition Among Large Tech Companies
			Knowledge for Virtual Personal Assistants
		12.6 Chatbots as Professional Advisors (Robo Advisors)
			Robo Financial Advisors
			Evolution of Financial Robo Advisors
			Robo Advisors 2.0: Adding the Human Touch
		0 Application Case 12.7 Betterment, the Pioneer of Financial Robo Advisors
			Managing Mutual Funds Using AI
			Other Professional Advisors
			IBM Watson
		12.7 Implementation Issues
			Technology Issues
			Disadvantages and Limitations of Bots
			Quality of Chatbots
			Setting Up Alexa’s Smart Home System
			Constructing Bots
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		References
	Chapter 13 The Internet of Things as a Platform for Intelligent Applications
		13.1 Opening Vignette: Cnh Industrial Uses the Internet of Things to Excel
		13.2 Essentials of IoT
			Definitions and Characteristics
			The IoT Ecosystem
			Structure of IoT Systems
		13.3 Major Benefits and Drivers of IoT
			Major Benefits of IoT
			Major Drivers of IoT
			Opportunities
		13.4 How IoT Works
			IoT and Decision Support
		13.5 Sensors and Their Role in IoT
			Brief Introduction to Sensor Technology
		0 Application Case 13.1 Using Sensors, IoT, and AI for Environmental Control at the Athens, Greece, International Airport
			How Sensors Work with IoT
		0 Application Case 13.2 Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets to Predict Failures
			Sensor Applications and Radio-Frequency Identification (RFID) Sensors
		13.6 Selected IoT Applications
			A Large-scale IoT in Action
			Examples of Other Existing Applications
		13.7 Smart Homes and Appliances
			Typical Components of Smart Homes
			Smart Appliances
			A Smart Home Is Where the Bot Is
			Barriers to Smart Home Adoption
		13.8 Smart Cities and Factories
		0 Application Case 13.3 Amsterdam on the Road to Become a Smart City
			Smart Buildings: From Automated to Cognitive Buildings
			Smart Components in Smart Cities and Smart Factories
		0 Application Case 13.4 How IBM Is Making Cities Smarter Worldwide
			Improving Transportation in the Smart City
			Combining Analytics and IoT in Smart City Initiatives
			Bill Gates’ Futuristic Smart City
			Technology Support for Smart Cities
		13.9 Autonomous (Self-Driving) Vehicles
			The Developments of Smart Vehicles
		0 Application Case 13.5 Waymo and Autonomous Vehicles
			Flying Cars
			Implementation Issues in Autonomous Vehicles
		13.10 Implementing IoT and Managerial Considerations
			Major Implementation Issues
			Strategy for Turning Industrial IoT into Competitive Advantage
			The Future of the IoT
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		References
Part V Caveats of Analytics and AI
	Chapter 14 Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts
		14.1 Opening Vignette: Why Did Uber Pay $245 Million to Waymo?
		14.2 Implementing Intelligent Systems: An Overview
			The Intelligent Systems Implementation Process
			The Impacts of Intelligent Systems
		14.3 Legal, Privacy, and Ethical Issues
			Legal Issues
			Privacy Issues
			Who Owns Our Private Data?
			Ethics Issues
			Ethical Issues of Intelligent Systems
			Other Topics in Intelligent Systems Ethics
		14.4 Successful Deployment of Intelligent Systems
			Top Management and Implementation
			System Development Implementation Issues
			Connectivity and Integration
			Security Protection
			Leveraging Intelligent Systems in Business
			Intelligent System Adoption
		14.5 Impacts of Intelligent Systems on Organizations
			New Organizational Units and Their Management
			Transforming Businesses and Increasing Competitive Advantage
		Application Case 14.1 How 1-800-Flowers.com Uses Intelligent Systems for Competitive Advantage
			Redesign of an Organization Through the Use of Analytics
			Intelligent Systems’ Impact on Managers’ Activities, Performance, and Job Satisfaction
			Impact on Decision Making
			Industrial Restructuring
		14.6 Impacts on Jobs and Work
			An Overview
			Are Intelligent Systems Going to Take Jobs—My Job?
			AI Puts Many Jobs at Risk
		Application Case 14.2 White-Collar Jobs That Robots Have Already Taken
			Which Jobs Are Most in Danger? Which Ones Are Safe?
			Intelligent Systems May Actually Add Jobs
			Jobs and the Nature of Work Will Change
			Conclusion: Let’s Be Optimistic!
		14.7 Potential Dangers of Robots, AI, and Analytical Modeling
			Position of AI Dystopia
			The AI Utopia’s Position
			The Open AI Project and the Friendly AI
			The O’Neil Claim of Potential Analytics’ Dangers
		14.8 Relevant Technology Trends
			Gartner’s Top Strategic Technology Trends for 2018 and 2019
			Other Predictions Regarding Technology Trends
			Summary: Impact on AI and Analytics
			Ambient Computing (Intelligence)
		14.9 Future of Intelligent Systems
			What Are the Major U.S. High-Tech Companies Doing in the Intelligent Technologies Field?
			AI Research Activities in China
		0 Application Case 14.3 How Alibaba.com Is Conducting AI
			The U.S.–China Competition: Who Will Control AI?
			The Largest Opportunity in Business
			Conclusion
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		References
Glossary
Index
Back Cover