In the opening vignette on sports analytics, what was adjusted to drive one-time ticket sales?

Robotics, Social Networks, AI and IoT 579

Caveats of

Analytics and AI

725

Chapter 14

Implementation Issues: From Ethics and Privacy to Organizational and Societal

Impacts 726

Glossary 770 Index 785

iii

Preface xxv

About the Authors xxxiv

Introduction to Analytics and AI 1

Chapter 1 Overview of Business Intelligence, Analytics, Data

Science, and Artificial Intelligence: Systems for Decision

Support 2

1.1 Opening Vignette: How Intelligent Systems Work for KONE Elevators and

Escalators Company 3

1.2 Changing Business Environments and Evolving Needs for

Decision Support and Analytics 5

Decision-Making Process 6

The Influence of the External and Internal Environments on the Process 6

Data and Its Analysis in Decision Making 7

Technologies for Data Analysis and Decision Support 7

1.3 Decision-Making Processes and Computerized Decision Support Framework 9

Simon’s Process: Intelligence, Design, and Choice 9

The Intelligence Phase: Problem (or Opportunity) Identification 10

0 APPLICATION CASE 1.1 Making Elevators Go Faster! 11

The Design Phase 12

The Choice Phase 13

The Implementation Phase 13

The Classical Decision Support System Framework 14

PART IV

Chapter 10 Robotics: Industrial and Consumer Applications 580

Chapter 11 Group Decision Making, Collaborative Systems, and AI

Support 610

Chapter 12 K nowledge Systems: Expert Systems, Recommenders,

Chatbots, Virtual Personal Assistants, and Robo A

dvisors 648

Chapter 13 The Internet of Things as a Platform for Intelligent

Applications 687

PART V

PART I

A DSS Application 16

Components of a Decision Support System 18

The Data Management Subsystem 18

The Model Management Subsystem 19

0 APPLICATION CASE 1.2 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions 20

The User Interface Subsystem 20

The Knowledge-Based Management Subsystem 21

1.4 Evolution of Computerized Decision Support to Business Intelligence/Analytics/Data Science 22

A Framework for Business Intelligence 25

The Architecture of BI 25

The Origins and Drivers of BI 26

Data Warehouse as a Foundation for Business Intelligence 27

Transaction Processing versus Analytic

Processing 27 A Multimedia Exercise in

Business Intelligence 28

iv

v Contents

1.5 Analytics Overview 30

Descriptive Analytics 32

0 APPLICATION CASE 1.3 Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities 32

0 APPLICATION CASE 1.4 Siemens Reduces Cost with the Use of Data Visualization 33

Predictive Analytics 33

0 APPLICATION CASE 1.5 Analyzing Athletic Injuries 34

Prescriptive Analytics 34

0 APPLICATION CASE 1.6 A Specialty Steel Bar Company Uses Analytics to Determine

Available-to-Promise Dates 35

1.6 Analytics Examples in Selected Domains 38

Sports Analytics—An Exciting Frontier for Learning and Understanding

Applications of Analytics 38

Analytics Applications in Healthcare—Humana Examples 43

0 APPLICATION CASE 1.7 Image Analysis Helps Estimate Plant Cover 50

1.7 Artificial Intelligence Overview 52

What Is Artificial Intelligence? 52

The Major Benefits of AI 52

The Landscape of AI 52 0 APPLICATION CASE 1.8 AI Increases Passengers’ Comfort and

Security in Airports and Borders 54

The Three Flavors of AI Decisions 55

Autonomous AI 55

Societal Impacts 56 0 APPLICATION CASE 1.9 Robots Took the Job of Camel-Racing Jockeys for Societal Benefits

58

1.8 Convergence of Analytics and AI 59

Major Differences between Analytics and AI 59

Why Combine Intelligent Systems? 60

How Convergence Can Help? 60

Big Data Is Empowering AI Technologies 60

The Convergence of AI and the IoT 61

The Convergence with Blockchain and Other Technologies 62

0 APPLICATION CASE 1.10 Amazon Go Is Open for Business 62 IBM and Microsoft

Support for Intelligent Systems Convergence 63

1.9 Overview of the Analytics Ecosystem 63

1.10 Plan of the Book 65

1.11 Resources, Links, and the Teradata University Network Connection 66

Resources and Links 66

Vendors, Products, and Demos 66

Periodicals 67

The Teradata University Network Connection 67

vi Contents

The Book’s Web Site 67

Chapter Highlights 67 • Key Terms 68

Questions for Discussion 68 • Exercises 69 References 70

Chapter 2 Artificial Intelligence: Concepts, Drivers, Major

Technologies, and Business Applications 73

2.1 Opening Vignette: INRIX Solves Transportation Problems 74

2.2 Introduction to Artificial Intelligence 76

Definitions 76

Major Characteristics of AI Machines 77

Major Elements of AI 77

AI Applications 78

Major Goals of AI 78

Drivers of AI 79

Benefits of AI 79

Some Limitations of AI Machines 81

Three Flavors of AI Decisions 81

Artificial Brain 82

2.3 Human and Computer Intelligence 83

What Is Intelligence? 83

How Intelligent Is AI? 84

Measuring AI 85

0 APPLICATION CASE 2.1 How Smart Can a Vacuum Cleaner Be? 86

2.4 Major AI Technologies and Some Derivatives 87

Intelligent Agents 87

Machine Learning 88

0 APPLICATION CASE 2.2 How Machine Learning Is Improving Work in

Business 89

Machine and Computer Vision 90

Robotic Systems 91

Natural Language Processing 92

Knowledge and Expert Systems and Recommenders 93

Chatbots 94

Emerging AI Technologies 94

2.5 AI Support for Decision Making 95

Some Issues and Factors in Using AI in Decision Making 96

AI Support of the Decision-Making Process 96

Automated Decision Making 97

0 APPLICATION CASE 2.3 How Companies Solve Real-World Problems Using Google’s Machine-Learning Tools 97

Conclusion 98

Contents vii

2.6 AI Applications in Accounting 99

AI in Accounting: An Overview 99

AI in Big Accounting Companies 100

Accounting Applications in Small Firms 100

0 APPLICATION CASE 2.4 How EY, Deloitte, and PwC Are Using AI 100 Job of Accountants

101

2.7 AI Applications in Financial Services 101

AI Activities in Financial Services 101

AI in Banking: An Overview 101

Illustrative AI Applications in Banking 102

Insurance Services 103 0 APPLICATION CASE 2.5 US Bank Customer Recognition and Services 104

2.8 AI in Human Resource Management (HRM) 105

AI in HRM: An Overview 105

AI in Onboarding 105 0 APPLICATION CASE 2.6 How Alexander Mann Solutions (AMS) Is

Using AI to Support the Recruiting Process 106

Introducing AI to HRM Operations 106

2.9 AI in Marketing, Advertising, and CRM 107

Overview of Major Applications 107

AI Marketing Assistants in Action 108

Customer Experiences and CRM 108

0 APPLICATION CASE 2.7 Kraft Foods Uses AI for Marketing and CRM 109

Other Uses of AI in Marketing 110

2.10 AI Applications in Production-Operation Management (POM) 110

AI in Manufacturing 110

Implementation Model 111

Intelligent Factories 111

Logistics and Transportation 112 Chapter Highlights 112 • Key Terms 113

Questions for Discussion 113 • Exercises 114 References 114

Chapter 3 Nature of Data, Statistical Modeling, and Visualization 117

3.1 Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio

Consumers with Data-Driven Marketing 118

3.2 Nature of Data 121

3.3 Simple Taxonomy of Data 125

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 127

Contents

3.4 Art and Science of Data Preprocessing 129

0 APPLICATION CASE 3.2 Improving Student Retention with Data-

Driven Analytics 133

3.5 Statistical Modeling for Business Analytics 139

viii

Descriptive Statistics for Descriptive Analytics 140

Measures of Centrality Tendency (Also Called Measures of Location

or Centrality) 140

Arithmetic Mean 140

Median 141

Mode 141

Measures of Dispersion (Also Called Measures of Spread or

Decentrality) 142

Range 142

Variance 142

Standard Deviation 143

Mean Absolute Deviation 143

Quartiles and Interquartile Range 143

Box-and-Whiskers Plot 143

Shape of a Distribution 145

0 APPLICATION CASE 3.3 Town of Cary Uses Analytics to Analyze Data

from Sensors, Assess Demand, and Detect Problems 150

3.6 Regression Modeling for Inferential Statistics 151

How Do We Develop the Linear Regression Model? 152

How Do We Know If the Model Is Good Enough? 153

What Are the Most Important Assumptions in Linear Regression? 154

Logistic Regression 155

Time-Series Forecasting 156

0 APPLICATION CASE 3.4 Predicting NCAA Bowl Game Outcomes 157

3.7 Business Reporting 163

0 APPLICATION CASE 3.5 Flood of Paper Ends at FEMA 165

3.8 Data Visualization 166

Brief History of Data Visualization 167

0 APPLICATION CASE 3.6 Macfarlan Smith Improves Operational Performance Insight with Tableau Online 169

3.9 Different Types of Charts and Graphs 171

Basic Charts and Graphs 171

Specialized Charts and Graphs 172

Which Chart or Graph Should You Use? 174

3.10 Emergence of Visual Analytics 176

Visual Analytics 178

High-Powered Visual Analytics Environments 180

3.11 Information Dashboards 182

0 APPLICATION CASE 3.7 Dallas Cowboys Score Big with Tableau and Teknion 184

Dashboard Design 184

0 APPLICATION CASE 3.8 Visual Analytics Helps Energy Supplier Make Better Connections 185

Contents ix

What to Look for in a Dashboard 186

Best Practices in Dashboard Design 187

Benchmark Key Performance Indicators with Industry Standards 187

Wrap the Dashboard Metrics with Contextual Metadata 187

Validate the Dashboard Design by a Usability Specialist 187

Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard 188

Enrich the Dashboard with Business-User Comments 188

Present Information in Three Different Levels 188

Pick the Right Visual Construct Using Dashboard Design Principles 188

Provide for Guided Analytics 188

Chapter Highlights 188 • Key Terms 189

Questions for Discussion 190 • Exercises 190 References 192

Predictive Analytics/Machine Learning 193

Chapter 4 Data Mining Process, Methods, and Algorithms 194

4.1 Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee

and Fight Crime 195

4.2 Data Mining Concepts 198

0 APPLICATION CASE 4.1 Visa Is Enhancing the Customer Experience while Reducing Fraud with Predictive Analytics and Data Mining 199

Definitions, Characteristics, and Benefits 201

How Data Mining Works 202

0 APPLICATION CASE 4.2 American Honda Uses Advanced Analytics to Improve Warranty Claims 203

Data Mining Versus Statistics 208

4.3 Data Mining Applications 208

0 APPLICATION CASE 4.3 Predictive Analytic and Data Mining Help Stop Terrorist Funding 210

4.4 Data Mining Process 211

Step 1: Business Understanding 212

Step 2: Data Understanding 212

Step 3: Data Preparation 213

Step 4: Model Building 214

0 APPLICATION CASE 4.4 Data Mining Helps in

Cancer Research 214

Step 5: Testing and Evaluation 217 Contents

Step 6: Deployment 217

Other Data Mining Standardized Processes and Methodologies 217

4.5 Data Mining Methods 220

Classification 220

Estimating the True Accuracy of Classification Models 221

Estimating the Relative Importance of Predictor Variables 224

PART II

x

Cluster Analysis for Data Mining 228

0 APPLICATION CASE 4.5 Influence Health Uses Advanced

Predictive Analytics to Focus on the Factors That Really Influence

People’s Healthcare Decisions 229

Association Rule Mining 232

4.6 Data Mining Software Tools 236

0 APPLICATION CASE 4.6 Data Mining goes to Hollywood: Predicting Financial Success of Movies 239

4.7 Data Mining Privacy Issues, Myths, and Blunders 242

0 APPLICATION CASE 4.7 Predicting Customer Buying Patterns—The Target Story 243

Data Mining Myths and Blunders 244

Chapter Highlights 246 • Key Terms 247

Questions for Discussion 247 • Exercises 248 References 250

Chapter 5 Machine-Learning Techniques for Predictive

Analytics 251

5.1 Opening Vignette: Predictive Modeling Helps Better Understand and Manage Complex Medical Procedures

252

5.2 Basic Concepts of Neural Networks 255

Biological versus Artificial Neural Networks 256

0 APPLICATION CASE 5.1 Neural Networks are Helping to

Save Lives in the Mining Industry 258

5.3 Neural Network Architectures 259

Kohonen’s Self-Organizing Feature Maps 259

Hopfield Networks 260

0 APPLICATION CASE 5.2 Predictive Modeling Is Powering the Power Generators 261

5.4 Support Vector Machines 263

0 APPLICATION CASE 5.3 Identifying Injury Severity Risk Factors in Vehicle Crashes with Predictive Analytics 264

Mathematical Formulation of SVM 269

Primal Form 269

Dual Form 269

Soft Margin 270

Nonlinear Classification 270

Kernel Trick 271

5.5 Process-Based Approach to the Use of SVM 271

Support Vector Machines versus Artificial Neural Networks 273

5.6 Nearest Neighbor Method for Prediction 274

Similarity Measure: The Distance Metric 275

Parameter Selection 275

0 APPLICATION CASE 5.4 Efficient Image Recognition and Categorization with knn 277

Contents xi

5.7 Naïve Bayes Method for Classification 278

Bayes Theorem 279

Naïve Bayes Classifier 279

Process of Developing a Naïve Bayes Classifier 280

Testing Phase 281 0 APPLICATION CASE 5.5 Predicting Disease Progress in Crohn’s Disease Patients: A

Comparison of Analytics Methods 282

5.8 Bayesian Networks 287 How Does BN Work? 287

How Can BN Be Constructed? 288

5.9 Ensemble Modeling 293

Motivation—Why Do We Need to Use Ensembles? 293

Different Types of Ensembles 295

Bagging 296

Boosting 298

Variants of Bagging and Boosting 299

Stacking 300

Information Fusion 300

Summary—Ensembles are not Perfect! 301

0 APPLICATION CASE 5.6 To Imprison or Not to Imprison: A Predictive Analytics-Based Decision Support System for Drug Courts 304

Chapter Highlights 306 • Key Terms 308

Questions for Discussion 308 • Exercises 309

Internet Exercises 312 • References 313

Chapter 6 Deep Learning and Cognitive Computing 315

6.1 Opening Vignette: Fighting Fraud with Deep Learning and Artificial Intelligence 316

6.2 Introduction to Deep Learning 320

0 APPLICATION CASE 6.1 Finding the Next Football Star with Artificial Intelligence 323

6.3 Basics of “Shallow” Neural Networks 325

0 APPLICATION CASE 6.2 Gaming Companies Use Data Analytics to Score Points with Players 328

0 APPLICATION CASE 6.3 Artificial Intelligence Helps Protect Animals from Extinction 333

xii Contents

6.4 Process of Developing Neural Network–Based Systems 334

Learning Process in ANN 335

Backpropagation for ANN Training 336

6.5 Illuminating the Black Box of ANN 340

0 APPLICATION CASE 6.4 Sensitivity Analysis Reveals Injury Severity Factors in Traffic Accidents 341

6.6 Deep Neural Networks 343

Feedforward Multilayer Perceptron (MLP)-Type Deep Networks 343

Impact of Random Weights in Deep MLP 344

More Hidden Layers versus More Neurons? 345 0 APPLICATION CASE 6.5 Georgia DOT Variable Speed Limit Analytics Help Solve Traffic Congestions

346

6.7 Convolutional Neural Networks 349

Convolution Function 349

Pooling 352

Image Processing Using Convolutional Networks 353 0 APPLICATION CASE 6.6 From Image Recognition to Face

Recognition 356

Text Processing Using Convolutional Networks 357

6.8 Recurrent Networks and Long Short-Term Memory

Networks 360

0 APPLICATION CASE 6.7 Deliver Innovation by Understanding Customer Sentiments 363

LSTM Networks Applications 365

6.9 Computer Frameworks for Implementation of Deep Learning 368 Torch 368

Caffe 368

TensorFlow 369

Theano 369

Keras: An Application Programming Interface 370

6.10 Cognitive Computing 370

How Does Cognitive Computing Work? 371

How Does Cognitive Computing Differ from AI? 372

Cognitive Search 374

IBM Watson: Analytics at Its Best 375 0 APPLICATION CASE 6.8 IBM Watson Competes against the

Best at Jeopardy! 376

How Does Watson Do It? 377

What Is the Future for Watson? 377

Chapter Highlights 381 • Key Terms 383

Questions for Discussion 383 • Exercises 384

References 385

Chapter 7 Text Mining, Sentiment Analysis, and Social Analytics 388

7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into Near-Real- Time Sales 389

Contents xiii

7.2 Text Analytics and Text Mining Overview 392

0 APPLICATION CASE 7.1 Netflix: Using Big Data to Drive Big Engagement: Unlocking the Power of Analytics to Drive

Content and Consumer Insight 395

7.3 Natural Language Processing (NLP) 397

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 399

7.4 Text Mining Applications 402

Marketing Applications 403

Security Applications 403

Biomedical Applications 404

0 APPLICATION CASE 7.3 Mining for Lies 404

Academic Applications 407

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 408

7.5 Text Mining Process 410

Task 1: Establish the Corpus 410

Task 2: Create the Term–Document Matrix 411

Task 3: Extract the Knowledge 413

0 APPLICATION CASE 7.5 Research Literature Survey with Text Mining 415

7.6 Sentiment Analysis 418

0 APPLICATION CASE 7.6 Creating a Unique Digital Experience to Capture Moments That Matter at Wimbledon 419

Sentiment Analysis Applications 422

Sentiment Analysis Process 424

Methods for Polarity Identification 426

Using a Lexicon 426

Using a Collection of Training Documents 427

Identifying Semantic Orientation of Sentences and Phrases 428

Identifying Semantic Orientation of Documents 428

7.7 Web Mining Overview 429

Web Content and Web Structure Mining 431

7.8 Search Engines 433

Anatomy of a Search Engine 434

1. Development Cycle 434

2. Response Cycle 435

Search Engine Optimization 436

Methods for Search Engine Optimization 437

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 439

7.9 Web Usage Mining (Web Analytics) 441

Web Analytics Technologies 441

Web Analytics Metrics 442

xiv Contents

Web Site Usability 442

Traffic Sources 443

Visitor Profiles 444

Conversion Statistics 444

7.10 Social Analytics 446

Social Network Analysis 446

Social Network Analysis Metrics 447

0 APPLICATION CASE 7.8 Tito’s Vodka Establishes Brand Loyalty with an Authentic Social Strategy 447

Connections 450

Distributions 450

Segmentation 451

Social Media Analytics 451

How Do People Use Social Media? 452

Measuring the Social Media Impact 453

Best Practices in Social Media Analytics 453

Chapter Highlights 455 • Key Terms 456

Questions for Discussion 456 • Exercises 456 References 457

Prescriptive Analytics and Big Data 459

Chapter 8 Prescriptive Analytics:

Optimization and Simulation 460

8.1 Opening Vignette: School District of Philadelphia Uses

Prescriptive Analytics to Find Optimal Solution for

Awarding Bus Route Contracts 461

8.2 Model-Based Decision Making 462

0 APPLICATION CASE 8.1 Canadian Football League Optimizes Game Schedule 463

Prescriptive Analytics Model Examples 465

Identification of the Problem and Environmental Analysis 465

0 APPLICATION CASE 8.2 Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions 466

Model Categories 467

8.3 Structure of Mathematical Models for Decision

Support 469

The Components of Decision Support Mathematical Models 469

The Structure of Mathematical Models 470

8.4 Certainty, Uncertainty, and Risk 471

Decision Making under Certainty 471

Decision Making under Uncertainty 472

Decision Making under Risk (Risk Analysis) 472

0 APPLICATION CASE 8.3 American Airlines Uses Should-Cost Modeling to Assess the Uncertainty of Bids for Shipment Routes 472

8.5 Decision Modeling with Spreadsheets 473

PART III

Contents xv

0 APPLICATION CASE 8.4 Pennsylvania Adoption Exchange Uses Spreadsheet Model to Better Match Children with Families 474

0 APPLICATION CASE 8.5 Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Routes 475

8.6 Mathematical Programming Optimization 477

0 APPLICATION CASE 8.6 Mixed-Integer Programming Model Helps the University of Tennessee Medical Center with Scheduling Physicians 478

Linear Programming Model 479

Modeling in LP: An Example 480

Implementation 484

8.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking 486

Multiple Goals 486

Sensitivity Analysis 487

What-If Analysis 488

Goal Seeking 489

8.8 Decision Analysis with Decision Tables and Decision Trees 490

Decision Tables 490

Decision Trees 492

8.9 Introduction to Simulation 493

Major Characteristics of Simulation 493

0 APPLICATION CASE 8.7 Steel Tubing Manufacturer Uses a Simulation-Based Production Scheduling System 493

Advantages of Simulation 494

Disadvantages of Simulation 495

The Methodology of Simulation 495

Simulation Types 496

Monte Carlo Simulation 497

Discrete Event Simulation 498 0 APPLICATION CASE 8.8 Cosan Improves Its Renewable Energy Supply Chain Using Simulation

498 8.10 Visual Interactive Simulation 500 Conventional Simulation Inadequacies 500

Visual Interactive Simulation 500

Visual Interactive Models and DSS 500

Simulation Software 501

0 APPLICATION CASE 8.9 Improving Job-Shop Scheduling Decisions through RFID: A Simulation-Based Assessment 501

Chapter Highlights 505 • Key Terms 505

Questions for Discussion 505 • Exercises 506 References 508

Chapter 9 Big Data, Cloud Computing, and Location Analytics: Concepts and

Tools 509

9.1 Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data

Methods 510

9.2 Definition of Big Data 513

The “V”s That Define Big Data 514

0 APPLICATION CASE 9.1 Alternative Data for Market Analysis or Forecasts 517

xvi Contents

9.3 Fundamentals of Big Data Analytics 519

Business Problems Addressed by Big Data Analytics 521 0 APPLICATION CASE 9.2 Overstock.com Combines Multiple Datasets to

Understand Customer Journeys 522

9.4 Big Data Technologies 523 MapReduce 523

Why Use MapReduce? 523

Hadoop 524

How Does Hadoop Work? 525

Hadoop Technical Components 525

Hadoop: The Pros and Cons 527

NoSQL 528 0 APPLICATION CASE 9.3 eBay’s Big Data Solution 529

0 APPLICATION CASE 9.4 Understanding Quality and Reliability of Healthcare Support Information on Twitter 531

9.5 Big Data and Data Warehousing 532

Use Cases for Hadoop 533

Use Cases for Data Warehousing 534

The Gray Areas (Any One of the Two Would Do the Job) 535

Coexistence of Hadoop and Data Warehouse 536

9.6 In-Memory Analytics and Apache Spark™ 537

0 APPLICATION CASE 9.5 Using Natural Language Processing to analyze customer feedback in TripAdvisor reviews 538 Architecture of Apache SparkTM 538

Getting Started with Apache SparkTM 539

9.7 Big Data and Stream Analytics 543

Stream Analytics versus Perpetual Analytics 544

Critical Event Processing 545

Data Stream Mining 546

Applications of Stream Analytics 546

e-Commerce 546

Telecommunications 546

0 APPLICATION CASE 9.6 Salesforce Is Using Streaming Data to

Enhance Customer Value 547

Law Enforcement and Cybersecurity 547

Power Industry 548

Financial Services 548

Health Sciences 548

Government 548

9.8 Big Data Vendors and Platforms 549

Infrastructure Services Providers 550

Analytics Solution Providers 550

Business Intelligence Providers Incorporating Big Data 551

0 APPLICATION CASE 9.7 Using Social Media for Nowcasting Flu Activity 551

0 APPLICATION CASE 9.8 Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse

554

9.9 Cloud Computing and Business Analytics 557

Data as a Service (DaaS) 558

Software as a Service (SaaS) 559

Platform as a Service (PaaS) 559

Infrastructure as a Service (IaaS) 559

Essential Technologies for Cloud Computing 560

0 APPLICATION CASE 9.9 Major West Coast Utility Uses Cloud-Mobile

Technology to Provide Real-Time Incident Reporting 561

Cloud Deployment Models 563

Chapter 9 • Big Data, Cloud Computing, and Location Analytics: Concepts and Tools 17

Major Cloud Platform Providers in Analytics 563

Analytics as a Service (AaaS) 564

Representative Analytics as a Service Offerings 564

Illustrative Analytics Applications Employing the Cloud Infrastructure 565

Using Azure IOT, Stream Analytics, and Machine

Learning to Improve Mobile

Health Care Services 565

Gulf Air Uses Big Data to Get Deeper Customer Insight 566

Chime Enhances Customer Experience Using Snowflake 566

9.10 Location-Based Analytics for Organizations 567

Geospatial Analytics 567 0 APPLICATION CASE 9.10 Great Clips Employs Spatial

Analytics to Shave Time in Location Decisions 570

0 APPLICATION CASE 9.11 Starbucks Exploits GIS and Analytics to Grow Worldwide 570

Real-Time Location Intelligence 572

Analytics Applications for Consumers 573

Chapter Highlights 574 • Key Terms 575

Questions for Discussion 575 • Exercises 575

References 576

Contents

Robotics, Social

Networks, AI and IoT

579

Chapter 10 Robotics: Industrial and

Consumer Applications 580

10.1 Opening Vignette: Robots Provide

Emotional

Support to Patients and

Children 581

10.2 Overview of Robotics 584

10.3 History of Robotics 584

10.4 Illustrative

Applications of Robotics 586

Changing Precision Technology 586

Adidas 586

BMW Employs Collaborative Robots 587

Tega 587

San Francisco Burger Eatery 588

Spyce 588

Mahindra & Mahindra Ltd. 589

Robots in the Defense Industry 589

Pepper 590

Da Vinci Surgical System 592

Snoo – A Robotic Crib 593

MEDi 593

Care-E Robot 593

AGROBOT 594

10.5 Components of Robots 595

10.6 Various Categories of Robots 596

10.7 Autonomous Cars:

Robots in Motion 597

Autonomous Vehicle Development 598

Issues with Self-Driving Cars 599

10.8 Impact of Robots on

Current and Future Jobs 600

10.9 Legal Implications of

Robots and Artificial Intelligence 603

Tort Liability 603

Patents 603

Property 604

Taxation 604

Practice of Law 604

Constitutional Law 605

Professional Certification 605

PART IV

18 Part III • Prescriptive Analytics and Big Data

Law Enforcement 605

Chapter

Highlight

s 606 •

Key

Terms

606

Questions

for

Discussio

n 606 •

Exercises

607

References 607

Chapter 11 Group Decision Making, Collaborative

Systems, and AI Support 610

11.1 Opening Vignette: Hendrick Motorsports

Excels with Collaborative Teams 611

11.2 Making Decisions in Groups: Characteristics, Process, Benefits,

and Dysfunctions 613

Characteristics of Group Work 613

Types of Decisions Made by Groups 614

Group Decision-Making Process 614

Benefits and Limitations of Group Work 615

11.3 Supporting Group Work and Team

Collaboration with Computerized

Systems 616

Overview of Group Support Systems (GSS) 617

Time/Place Framework 617

Group Collaboration for Decision Support 618

11.4 Electronic Support for Group

Communication and

Collaboration 619

Groupware for Group Collaboration 619

Synchronous versus Asynchronous Products 619

Virtual Meeting Systems 620

Collaborative Networks and Hubs 622

Collaborative Hubs 622

Social Collaboration 622

Sample of Popular Collaboration Software 623

11.5 Direct Computerized Support for Group

Decision

Making 623

Group Decision Support Systems (GDSS) 624

Characteristics of GDSS 625

Supporting the Entire Decision-Making Process 625

Brainstorming for Idea Generation and Problem Solving 627

Group Support Systems 628

11.6 Collective Intelligence and Collaborative Intelligence 629

Definitions and Benefits 629

Computerized Support to Collective Intelligence 629

0 APPLICATION CASE 11.1 Collaborative Modeling for Optimal

Water Management: The

Oregon State University

Project 630

How Collective Intelligence May Change Work and Life 631

Collaborative Intelligence 632

How to Create Business Value from

Collaboration: The IBM

Study 632 Contents

11.7 Crowdsourcing as a Method for Decision

Support 633

The Essentials of Crowdsourcing 633

Crowdsourcing for Problem-Solving and Decision Support 634

Implementing Crowdsourcing for Problem Solving 635

0 APPLICATION

CASE 11.2 How

InnoCentive

Helped GSK

Solve a

Chapter 9 • Big Data, Cloud Computing, and Location Analytics: Concepts and Tools 19

Difficult

Problem 636

11.8 Artificial

Intelligence and Swarm AI Support of Team

Collaboration and Group

Decision Making 636

AI Support of Group Decision Making 637

AI Support of Team Collaboration 637

Swarm Intelligence and Swarm AI 639

0 APPLICATION

CASE 11.3 XPRIZE Optimizes

Visioneering 639

11.9 Human–Machine Collaboration and Teams

of Robots 640

Human–Machine Collaboration in Cognitive Jobs 641

Robots as Coworkers: Opportunities and Challenges 641

Teams of collaborating Robots 642

Chapte

r

Highlig

hts 644

• Key

Terms

645

Questio

ns for

Discuss

ion 645

•

Exercis

es 645

Referen

ces 646

Chapter 12 Knowledge Systems:

Expert Systems, Recommenders,

Chatbots, Virtual

Personal

Assistants, and

Robo Advisors 648

12.1 Opening Vignette: Sephora Excels with

Chatbots 649

12.2 Expert Systems and

Recommenders 650

Basic Concepts of Expert Systems (ES) 650

Characteristics and Benefits of ES 652

Typical Areas for ES Applications 653

Structure and Process of ES 653

0 APPLICATION CASE 12.1 ES Aid in Identification of Chemical,

Biological, and Radiological Agents

655

Why the Classical Type of ES Is Disappearing 655

0 APPLICATION CASE 12.2 VisiRule 656

Recommendation Systems 657

0 APPLICATION

CASE 12.3 Netflix

Recommender

: A Critical

Success Factor

658

12.3 Concepts, Drivers,

and Benefits of Chatbots 660

What Is a Chatbot? 660

Chatbot Evolution 660

Components of Chatbots and the Process of Their Use 662

Drivers and Benefits 663

Representative Chatbots from Around the World 663

12.4 Enterprise Chatbots 664

20 Part III • Prescriptive Analytics and Big Data

The Interest of Enterprises in Chatbots 664

Enterprise Chatbots: Marketing and Customer Experience 665

0 APPLICATION CASE 12.4 WeChat’s Super Chatbot 666

0 APPLICATION CASE 12.5 How Vera

Gold Mark Uses Chatbots to Increase

Sales 667

Enterprise Chatbots: Financial Services 668

Enterprise Chatbots: Service Industries 668

Chatbot Platforms 669

0 APPLICATION CASE 12.6 Transavia

Airlines Uses Bots for

Communication and Customer Care

Delivery 669

Knowledge for Enterprise Chatbots 671

12.5 Virtual Personal Assistants 672

Assistant for Information Search 672

If You Were Mark Zuckerberg, Facebook CEO 672

Amazon’s Alexa and Echo 672

Apple’s Siri 675

Google Assistant 675

Other Personal Assistants 675

Competition Among Large Tech Companies 675

Knowledge for Virtual Personal Assistants 675

12.6 Chatbots as Professional Advisors (Robo

Advisors) 676

Robo Financial Advisors 676

Evolution of Financial Robo Advisors 676

Robo Advisors 2.0: Adding the Human Touch 676

0 APPLICATION CASE 12.7 Betterment,

the Pioneer of Financial Robo

Advisors 677

Managing Mutual Funds Using AI 678

Other Professional Advisors 678

IBM Watson 680

12.7 Implementation Issues 680

Technology Issues 680

Disadvantages and Limitations of Bots 681

Quality of Chatbots 681

Setting Up Alexa’s Smart Home System 682

Constructing Bots 682

Chapter Highlights 683 • Key Terms 683

Questions for Discussion 684 •

Exercises 684 References 685

Chapter 13 The Internet of Things as a Platform for

Intelligent Applications 687

13.1 Opening Vignette: CNH Industrial Uses the Internet of Things to Excel 688

13.2 Essentials of IoT 689

Definitions and Characteristics 690 Contents

The IoT Ecosystem 691

Structure of IoT Systems 691

13.3 Major Benefits and Drivers of IoT 694

Major Benefits of IoT 694

Major Drivers of IoT 695

Opportunities 695

13.4 How IoT Works 696

IoT and Decision Support 696

13.5 Sensors and Their Role in IoT 697

Brief Introduction to Sensor Technology 697

0 APPLICATION CASE 13.1 Using Sensors, IoT, and AI for

Environmental

Control at the

Athens, Greece,

International Airport

697

How Sensors Work with IoT 698

0 APPLICATION CASE 13.2 Rockwell Automation

Chapter 9 • Big Data, Cloud Computing, and Location Analytics: Concepts and Tools 21

Monitor

s

Expensiv

e Oil

and Gas

Explorat

ion

Assets

to

Predict

Failures

698

Sensor Applications and Radio-Frequency Identification (RFID) Sensors 699

13.6 Selected IoT Applications 701

A Large-scale IoT in Action 701

Examples of Other Existing Applications 701

13.7 Smart Homes and Appliances 703

Typical Components of Smart Homes 703

Smart Appliances 704

A Smart Home Is Where the Bot Is 706

Barriers to Smart Home Adoption 707

13.8 Smart Cities and Factories 707

0 APPLICATION

CASE 13.3

Amsterdam on

the Road to

Become a

Smart City 708

Smart Buildings: From Automated to Cognitive Buildings 709

Smart Components in Smart Cities and Smart Factories 709

0 APPLICATION

CASE 13.4 How IBM Is

Making Cities

Smarter

Worldwide 711

Improving Transportation in the Smart City 712

Combining Analytics and IoT in Smart City Initiatives 713

Bill Gates’ Futuristic Smart City 713

Technology Support for Smart Cities 713

13.9 Autonomous (Self- Driving) Vehicles 714

The Developments of Smart Vehicles 714

0 APPLICATION

CASE 13.5 Waymo and Autonomous

Vehicles 715

Flying Cars 717

Implementation Issues in Autonomous Vehicles 717

13.10 Implementing IoT and Managerial

Considerations 717

Major Implementation Issues 718

Strategy for Turning Industrial IoT into Competitive Advantage 719

The Future of the IoT 720

Chapter Highlights 721 • Key Terms 72

(accessed October 2018).

https://microsoft.github.io/techcasestudies/iot/2016/12/02/IoT-ZionChina.html

579

C H A P T E R

10

Robotics: Industrial and Consumer

Applications

hapter 2 briefly introduced robotics, an early and practical application of concepts developed in AI.

In this chapter, we present a number of applications of robots in industrial as well as personal settings.

Besides learning about the already deployed and emerging applications, we identify the general C

IV

LEARNING OBJECTIVES

■

■ Discuss the general history of automation and

robots

■

■ Discuss the applications of robots in various

industries

■

■ Differentiate between industrial and consumer

applications of robots

■

■ Identify common components of robots

■

■ Discuss impacts of robots on future jobs

■

■ Identify legal issues related to robotics

Chapter 10 • Robotics: Industrial and Consumer Applications 23

components of a robot. In the spirit of managerial considerations, we also discuss the impact of robotics on

jobs as well as related legal issues. Some of the coverage is broad and impacts all other artificial intelligence

(AI), so it may seem to overlap a bit with Chapter 14. But the focus in this chapter is on physical robots, not

just software-driven applications of AI.

This chapter has the following sections:

10.1 O pening Vignette: Robots Provide Emotional Support to Patients and Children 581

10.2 Overview of Robotics 584

10.3 History of Robotics 584

10.4 Illustrative Applications of Robotics 586

10.5 Components of Robots 595

10.6 Various Categories of Robots 596

10.7 Autonomous Cars: Robots in Motion 597

10.8 Impact of Robots on Current and Future Jobs 600

10.9 Legal Implications of Robots and Artificial Intelligence 603

580

10.1 OPENING VIGNETTE: Robots Provide Emotional Support

to Patients and Children As discussed in this chapter, robots have impacted industrial manufacturing and other physical activities. Now, with the research and

evolution of AI, robotics can straddle the social world. For example, hospitals today make an effort to give social and emotional support

to patients and their families. This support is especially sensitive when offering treatment to children. Children in a hospital are in an

unfamiliar environment with medical instruments attached to them, and in many cases, doctors may recommend movement restrictions.

This restriction leads to stress, anxiety, and depression in children and consequently in their family members. Hospitals try to provide

childcare support specialist or companion pet therapies to reduce the trauma. These therapies prepare children and their parents for

future treatment and provide them with temporary emotional support with their interactions. Due to the small number of such

specialists, there is a gap between demand and supply for childcare specialists. Also, it is not possible to provide pet therapy at many

centers due to the fear of allergies, dust, and bites that may cause the patient’s condition to be aggravated. To fill these gaps, the use of

social robots is being explored to resolve depression and anxiety among children. A study (Jeong et al., 2015) found that the physical

presence of a robot is more effective concerning emotional response as compared to a virtual machine interaction in a pediatric hospital

center.

Researchers have known for a long time (e.g., Goris et al., 2010) that more than 60 percent of human communication is not verbal

but rather occurs through facial expressions. Thus, a social robot has to be able to provide emotional communication like a child

specialist. One popular robot that is providing such support is Huggable. With the help of AI, Huggable is equipped to understand

facial expressions, temperament, g estures, and human cleverness. It is like a staff member added to the team of specialists who provide

children some general emotional health assistance.

Huggable looks like a teddy bear having a ringed arrangement. A furry soft body provides a childish look to it and hence is perceived

as a friend by the children. With its mechanical arms, Huggable can perform specific actions quickly. Rather than sporting high-tech

devices, a Huggable robot is composed of an Android device whose microphone, speaker, and camera are in its internal sensors, and a