In the opening vignette, predictive modeling is described as

iii

Preface xxv

About the Authors xxxiv

PART I Introduction to Analytics and AI 1 Chapter 1 Overview of Business Intelligence, Analytics,

Data Science, and Artificial Intelligence: Systems for Decision Support 2

Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications 73

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

PART II Predictive Analytics/Machine Learning 193 Chapter 4 Data Mining Process, Methods, and Algorithms 194

Chapter 5 Machine-Learning Techniques for Predictive Analytics 251

Chapter 6 Deep Learning and Cognitive Computing 315

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

PART III Prescriptive Analytics and Big Data 459 Chapter 8 Prescriptive Analytics: Optimization and

Simulation 460

Chapter 9 Big Data, Cloud Computing, and Location Analytics: Concepts and Tools 509

PART IV Robotics, Social Networks, AI and IoT 579 Chapter 10 Robotics: Industrial and Consumer Applications 580

Chapter 11 Group Decision Making, Collaborative Systems, and AI Support 610

Chapter 12 Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, and Robo Advisors 648

Chapter 13 The Internet of Things as a Platform for Intelligent Applications 687

PART V Caveats of Analytics and AI 725 Chapter 14 Implementation Issues: From Ethics and Privacy to

Organizational and Societal Impacts 726

Glossary 770

Index 785

BRIEF CONTENTS

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iv

CONTENTS

Preface xxv

About the Authors xxxiv

PART I 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

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

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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

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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

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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

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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

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

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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

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

PART II 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

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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

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

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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

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

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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

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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

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

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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

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

PART III 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

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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 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

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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

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

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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

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

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PART IV 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

Law Enforcement 605 Chapter Highlights 606 • Key Terms 606

Questions for Discussion 606 • Exercises 607

References 607

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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

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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

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 Chapter Highlights 644 • Key Terms 645

Questions for Discussion 645 • Exercises 645

References 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

The Interest of Enterprises in Chatbots 664

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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

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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

Monitors Expensive Oil and Gas Exploration 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

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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 721

Questions for Discussion 722 • Exercises 722

References 722

PART V Caveats of Analytics and AI 725

Chapter 14 Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 726 14.1 Opening Vignette: Why Did Uber Pay $245 Million to

Waymo? 727

14.2 Implementing Intelligent Systems: An Overview 729

The Intelligent Systems Implementation Process 729

The Impacts of Intelligent Systems 730

14.3 Legal, Privacy, and Ethical Issues 731

Legal Issues 731

Privacy Issues 732

Who Owns Our Private Data? 735

Ethics Issues 735

Ethical Issues of Intelligent Systems 736

Other Topics in Intelligent Systems Ethics 736

14.4 Successful Deployment of Intelligent Systems 737

Top Management and Implementation 738

System Development Implementation Issues 738

Connectivity and Integration 739

Security Protection 739

Leveraging Intelligent Systems in Business 739

Intelligent System Adoption 740

14.5 Impacts of Intelligent Systems on Organizations 740

New Organizational Units and Their Management 741

Transforming Businesses and Increasing Competitive Advantage 741 0 APPLICATION CASE 14.1 How 1-800-Flowers.com Uses Intelligent

Systems for Competitive Advantage 742

Redesign of an Organization Through the Use of Analytics 743

Intelligent Systems’ Impact on Managers’ Activities, Performance, and Job Satisfaction 744

Impact on Decision Making 745

Industrial Restructuring 746

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14.6 Impacts on Jobs and Work 747

An Overview 747

Are Intelligent Systems Going to Take Jobs—My Job? 747

AI Puts Many Jobs at Risk 748 0 APPLICATION CASE 14.2 White-Collar Jobs That Robots Have

Already Taken 748

Which Jobs Are Most in Danger? Which Ones Are Safe? 749

Intelligent Systems May Actually Add Jobs 750

Jobs and the Nature of Work Will Change 751

Conclusion: Let’s Be Optimistic! 752

14.7 Potential Dangers of Robots, AI, and Analytical Modeling 753

Position of AI Dystopia 753

The AI Utopia’s Position 753

The Open AI Project and the Friendly AI 754

The O’Neil Claim of Potential Analytics’ Dangers 755

14.8 Relevant Technology Trends 756

Gartner’s Top Strategic Technology Trends for 2018 and 2019 756

Other Predictions Regarding Technology Trends 757

Summary: Impact on AI and Analytics 758

Ambient Computing (Intelligence) 758

14.9 Future of Intelligent Systems 760

What Are the Major U.S. High-Tech Companies Doing in the Intelligent Technologies Field? 760

AI Research Activities in China 761 0 APPLICATION CASE 14.3 How Alibaba.com Is Conducting AI 762

The U.S.–China Competition: Who Will Control AI? 764

The Largest Opportunity in Business 764

Conclusion 764 Chapter Highlights 765 • Key Terms 766

Questions for Discussion 766 • Exercises 766

References 767

Glossary 770

Index 785

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xxv

PREFACE

Analytics has become the technology driver of this decade. Companies such as IBM, Oracle, Microsoft, and others are creating new organizational units focused on analytics that help businesses become more effective and efficient in their operations. Decision makers are using data and computerized tools to make better decisions. Even consumers are using analytics tools directly or indirectly to make decisions on routine activities such as shopping, health care, and entertainment. The field of business analytics (BA)/data sci- ence (DS)/decision support systems (DSS)/business intelligence (BI) is evolving rapidly to become more focused on innovative methods and applications to utilize data streams that were not even captured some time back, much less analyzed in any significant way. New applications emerge daily in customer relationship management, banking and fi- nance, health care and medicine, sports and entertainment, manufacturing and supply chain management, utilities and energy, and virtually every industry imaginable.

The theme of this revised edition is analytics, data science, and AI for enterprise decision support. In addition to traditional decision support applications, this edition ex- pands the reader’s understanding of the various types of analytics by providing examples, products, services, and exercises by means of introducing AI, machine-learning, robotics, chatbots, IoT, and Web/Internet-related enablers throughout the text. We highlight these technologies as emerging components of modern-day business analytics systems. AI tech- nologies have a major impact on decision making by enabling autonomous decisions and by supporting steps in the process of making decisions. AI and analytics support each other by creating a synergy that assists decision making.

The purpose of this book is to introduce the reader to the technologies that are generally and collectively called analytics (or business analytics) but have been known by other names such as decision support systems, executive information systems, and business intelligence, among others. We use these terms interchangeably. This book pres- ents the fundamentals of the methods, methodologies, and techniques used to design and develop these systems. In addition, we introduce the essentials of AI both as it relates to analytics as well as a standalone discipline for decision support.

We follow an EEE approach to introducing these topics: Exposure, Experience, and Explore. The book primarily provides exposure to various analytics techniques and their applications. The idea is that a student will be inspired to learn from how other organizations have employed analytics to make decisions or to gain a competitive edge. We believe that such exposure to what is being done with analytics and how it can be achieved is the key component of learning about analytics. In describing the techniques, we also introduce specific software tools that can be used for developing such applica- tions. The book is not limited to any one software tool, so the students can experience these techniques using any number of available software tools. Specific suggestions are given in each chapter, but the student and the professor are able to use this book with many different software tools. Our book’s companion Web site will include specific soft- ware guides, but students can gain experience with these techniques in many different ways. Finally, we hope that this exposure and experience enable and motivate read- ers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct them to Teradata University Network and other sites as well that include team-oriented exercises where appropriate. In our own teaching experience, projects undertaken in the class facilitate such exploration after the students have been exposed to the myriad of applications and concepts in the book and they have experienced specific software introduced by the professor.

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xxvi Preface

This edition of the book can be used to offer a one-semester overview course on analytics, which covers most or all of the topics/chapters included in the book. It can also be used to teach two consecutive courses. For example, one course could focus on the overall analytics coverage. It could cover selective sections of Chapters 1 and 3–9. A second course could focus on artificial intelligence and emerging technologies as the enablers of modern-day analytics as a subsequent course to the first course. This second course could cover portions of Chapters 1, 2, 6, 9, and 10–14. The book can be used to offer managerial-level exposure to applications and techniques as noted in the previous paragraph, but it also includes sufficient technical details in selected chapters to allow an instructor to focus on some technical methods and hands-on exercises.

Most of the specific improvements made in this eleventh edition concentrate on three areas: reorganization, content update/upgrade (including AI, machine-learning, chatbots, and robotics as enablers of analytics), and a sharper focus. Despite the many changes, we have preserved the comprehensiveness and user friendliness that have made the textbook a market leader in the last several decades. We have also optimized the book’s size and content by eliminating older and redundant material and by adding and combining material that is parallel to the current trends and is also demanded by many professors. Finally, we present accurate and updated material that is not available in any other text. We next describe the changes in the eleventh edition.

The book is supported by a Web site (pearsonhighered.com/sharda). We provide links to additional learning materials and software tutorials through a special section of the book Web site.

WHAT’S NEW IN THE ELEVENTH EDITION?

With the goal of improving the text and making it current with the evolving technology trends, this edition marks a major reorganization to better reflect on the current focus on analytics and its enabling technologies. The last three editions transformed the book from the traditional DSS to BI and then from BI to BA and fostered a tight linkage with the Teradata University Network (TUN). This edition is enhanced with new materials parallel- ing the latest trends in analytics including AI, machine learning, deep learning, robotics, IoT, and smart/robo-collaborative assisting systems and applications. The following sum- marizes the major changes made to this edition.

• New organization. The book is now organized around two main themes: (1) presentation of motivations, concepts, methods, and methodologies for different types of analytics (focusing heavily on predictive and prescriptive analytic), and (2) introduction and due coverage of new technology trends as the enablers of the modern-day analytics such as AI, machine learning, deep learning, robotics, IoT, smart/robo-collaborative assisting systems, etc. Chapter 1 provides an introduction to the journey of decision support and enabling technologies. It begins with a brief overview of the classical decision making and decision support systems. Then it moves to business intelligence, followed by an introduction to analytics, Big Data, and AI. We follow that with a deeper introduction to artificial intelligence in Chapter 2. Because data is fundamental to any analysis, Chapter 3 introduces data issues as well as descriptive analytics including statistical concepts and visualization. An on- line chapter covers data warehousing processes and fundamentals for those who like to dig deeper into these issues. The next section covers predictive analytics and machine learning. Chapter 4 provides an introduction to data mining applications and the data mining process. Chapter 5 introduces many of the common data min- ing techniques: classification, clustering, association mining, and so forth. Chapter 6 includes coverage of deep learning and cognitive computing. Chapter 7 focuses on

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text mining applications as well as Web analytics, including social media analytics, sentiment analysis, and other related topics. The following section brings the “data science” angle to a further depth. Chapter 8 covers prescriptive analytics including optimization and simulation. Chapter 9 includes more details of Big Data analytics. It also includes introduction to cloud-based analytics as well as location analytics. The next section covers Robotics, social networks, AI, and the Internet of Things (IoT). Chapter 10 introduces robots in business and consumer applications and also stud- ies the future impact of such devices on society. Chapter 11 focuses on collaboration systems, crowdsourcing, and social networks. Chapter 12 reviews personal assis- tants, chatbots, and the exciting developments in this space. Chapter 13 studies IoT and its potential in decision support and a smarter society. The ubiquity of wireless and GPS devices and other sensors is resulting in the creation of massive new data- bases and unique applications. Finally, Chapter 14 concludes with a brief discussion of security, privacy, and societal dimensions of analytics and AI.

We should note that several chapters included in this edition have been avail- able in the following companion book: Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson (2018) (Hereafter referred to as BI4e). The structure and contents of these chapters have been updated somewhat before inclusion in this edition of the book, but the changes are more significant in the chapters marked as new. Of course, several of the chapters that came from BI4e were not included in previous editions of this book.

• New chapters. The following chapters have been added:

Chapter 2 “Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications” This chapter covers the essentials of AI, outlines its benefits, compares it with humans’ intelligence, and describes the content of the field. Example applications in accounting, finance, human resource management, marketing and CRM, and production-operation management illustrate the benefits to business (100% new material) Chapter 6, “Deep Learning and Cognitive Computing” This chapter covers the generation of machine learning technique, deep learning as well as the increasingly more popular AI topic, cognitive computing. It is an almost entirely new chapter (90% new material). Chapter 10, “Robotics: Industrial and Consumer Applications” This chapter introduces many robotics applications in industry and for consumers and concludes with impacts of such advances on jobs and some legal ramifications (100% new material). Chapter 12, “Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, and Robo Advisors” This new chapter concentrates on different types of knowledge systems. Specifically, we cover new generations of expert systems and recommenders, chatbots, enterprise chatbots, virtual personal assistants, and robo-advisors (95% new). Chapter 13, “The Internet of Things as a Platform for Intelligent Applications” This new chapter introduces IoT as an enabler to analytics and AI applications. The following technologies are described in detail: smart homes and appliances, smart cities (including factories), and autonomous vehicles (100% new). Chapter 14, “Implementation Issues: From Ethics and Privacy to Organiza- tional and Societal Impacts” This mostly new chapter deals with implementation issues of intelligent systems (including analytics). The major issues covered are protection of privacy, intellectual property, ethics, technical issues (e.g., integration and security) and administrative issues. We also cover the impact of these technolo- gies on organizations and people and specifically deal with the impact on work and

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xxviii Preface

jobs. Special attention is given to possible unintended impacts of analytics and AI (robots). Then we look at relevant technology trends and conclude with an assess- ment of the future of analytics and AI (85% new).

• Streamlined coverage. We have optimized the book size and content by add- ing a lot of new material to cover new and cutting-edge analytics and AI trends and technologies while eliminating most of the older, less-used material. We use a dedicated Web site for the textbook to provide some of the older material as well as updated content and links.

• Revised and updated content. Several chapters have new opening vignettes that are based on recent stories and events. In addition, application cases throughout the book are new or have been updated to include recent examples of applications of a specific technique/model. These application case stories now include suggested questions for discussion to encourage class discussion as well as further explora- tion of the specific case and related materials. New Web site links have been added throughout the book. We also deleted many older product links and references. Finally, most chapters have new exercises, Internet assignments, and discussion questions throughout. The specific changes made to each chapter are as follows: Chapters 1, 3–5, and 7–9 borrow material from BI4e to a significant degree.

Chapter 1, “Overview of Business Intelligence, Analytics, Data Science, and Artifi- cial Intelligence: Systems for Decision Support” This chapter includes some material from DSS10e Chapters 1 and 2, but includes several new application cases, entirely new material on AI, and of course, a new plan for the book (about 50% new material).

Chapter 3, “Nature of Data, Statistical Modeling, and Visualization” • 75% new content. • Most of the content related to nature of data and statistical analysis is new. • New opening case. • Mostly new cases throughout.

Chapter 4, “Data Mining Process, Methods, and Algorithms” • 25% of the material is new. • Some of the application cases are new.

Chapter 5, “Machine Learning Techniques for Predictive Analytics” • 40% of the material is new. • New machine-learning methods: naïve Bayes, Bayesian networks, and ensemble

modeling. • Most of the cases are new.

Chapter 7, “Text Mining, Sentiment Analysis, and Social Analytics” • 25% of the material is new. • Some of the cases are new.

Chapter 8, “Prescriptive Analytics: Optimization and Simulation” • Several new optimization application exercises are included. • A new application case is included. • 20% of the material is new.

Chapter 9, “Big Data, Cloud Computing, and Location Analytics: Concepts and Tools” This material has bene updated substantially in this chapter to include greater coverage of stream analytics. It also updates material from Chapters 7 and 8 from BI4e (50% new material).

Chapter 11, “Group Decision Making, Collaborative Systems, and AI Support” The chapter is completely revised, regrouping group decision support. New topics include

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Preface xxix

collective and collaborative intelligence, crowdsourcing, swarm AI, and AI support of all related activities (80% new material).

We have retained many of the enhancements made in the last editions and updated the content. These are summarized next:

• Links to Teradata University Network (TUN). Most chapters include new links to TUN (teradatauniversitynetwork.com). We encourage the instructors to reg- ister and join teradatauniversitynetwork.com and explore the various content available through the site. The cases, white papers, and software exercises available through TUN will keep your class fresh and timely.

• Book title. As is already evident, the book’s title and focus have changed. • Software support. The TUN Web site provides software support at no charge.

It also provides links to free data mining and other software. In addition, the site provides exercises in the use of such software.

THE SUPPLEMENT PACKAGE: PEARSONHIGHERED.COM/SHARDA

A comprehensive and flexible technology-support package is available to enhance the teaching and learning experience. The following instructor and student supplements are available on the book’s Web site, pearsonhighered.com/sharda:

• Instructor’s Manual. The Instructor’s Manual includes learning objectives for the entire course and for each chapter, answers to the questions and exercises at the end of each chapter, and teaching suggestions (including instructions for projects). The Instructor’s Manual is available on the secure faculty section of pearsonhigh- ered.com/sharda.

• Test Item File and TestGen Software. The Test Item File is a comprehensive collection of true/false, multiple-choice, fill-in-the-blank, and essay questions. The questions are rated by difficulty level, and the answers are referenced by book page number. The Test Item File is available in Microsoft Word and in TestGen. Pear- son Education’s test-generating software is available from www.pearsonhighered. com/irc. The software is PC/MAC compatible and preloaded with all of the Test Item File questions. You can manually or randomly view test questions and drag- and-drop to create a test. You can add or modify test-bank questions as needed. Our TestGens are converted for use in BlackBoard, WebCT, Moodle, D2L, and Angel. These conversions can be found on pearsonhighered.com/sharda. The TestGen is also available in Respondus and can be found on www.respondus.com.

• PowerPoint slides. PowerPoint slides are available that illuminate and build on key concepts in the text. Faculty can download the PowerPoint slides from pear- sonhighered.com/sharda.

ACKNOWLEDGMENTS

Many individuals have provided suggestions and criticisms since the publication of the first edition of this book. Dozens of students participated in class testing of various chap- ters, software, and problems and assisted in collecting material. It is not possible to name everyone who participated in this project, but our thanks go to all of them. Certain indi- viduals made significant contributions, and they deserve special recognition.

First, we appreciate the efforts of those individuals who provided formal reviews of the first through eleventh editions (school affiliations as of the date of review):

Robert Blanning, Vanderbilt University Ranjit Bose, University of New Mexico

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xxx Preface

Warren Briggs, Suffolk University Lee Roy Bronner, Morgan State University Charles Butler, Colorado State University Sohail S. Chaudry, University of Wisconsin–La Crosse Kathy Chudoba, Florida State University Wingyan Chung, University of Texas Woo Young Chung, University of Memphis Paul “Buddy” Clark, South Carolina State University Pi’Sheng Deng, California State University–Stanislaus Joyce Elam, Florida International University Kurt Engemann, Iona College Gary Farrar, Jacksonville University George Federman, Santa Clara City College Jerry Fjermestad, New Jersey Institute of Technology Joey George, Florida State University Paul Gray, Claremont Graduate School Orv Greynholds, Capital College (Laurel, Maryland) Martin Grossman, Bridgewater State College Ray Jacobs, Ashland University Leonard Jessup, Indiana University Jeffrey Johnson, Utah State University Jahangir Karimi, University of Colorado Denver Saul Kassicieh, University of New Mexico Anand S. Kunnathur, University of Toledo Shao-ju Lee, California State University at Northridge Yair Levy, Nova Southeastern University Hank Lucas, New York University Jane Mackay, Texas Christian University George M. Marakas, University of Maryland Dick Mason, Southern Methodist University Nick McGaughey, San Jose State University Ido Millet, Pennsylvania State University–Erie Benjamin Mittman, Northwestern University Larry Moore, Virginia Polytechnic Institute and State University Simitra Mukherjee, Nova Southeastern University Marianne Murphy, Northeastern University Peter Mykytyn, Southern Illinois University Natalie Nazarenko, SUNY College at Fredonia David Olson, University of Nebraska Souren Paul, Southern Illinois University Joshua Pauli, Dakota State University Roger Alan Pick, University of Missouri–St. Louis Saeed Piri, University of Oregon W. “RP” Raghupaphi, California State University–Chico Loren Rees, Virginia Polytechnic Institute and State University David Russell, Western New England College Steve Ruth, George Mason University Vartan Safarian, Winona State University Glenn Shephard, San Jose State University Jung P. Shim, Mississippi State University Meenu Singh, Murray State University Randy Smith, University of Virginia

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Preface xxxi

James T. C. Teng, University of South Carolina John VanGigch, California State University at Sacramento David Van Over, University of Idaho Paul J. A. van Vliet, University of Nebraska at Omaha B. S. Vijayaraman, University of Akron Howard Charles Walton, Gettysburg College Diane B. Walz, University of Texas at San Antonio Paul R. Watkins, University of Southern California Randy S. Weinberg, Saint Cloud State University Jennifer Williams, University of Southern Indiana Selim Zaim, Sehir University Steve Zanakis, Florida International University Fan Zhao, Florida Gulf Coast University Hamed Majidi Zolbanin, Ball State University

Several individuals contributed material to the text or the supporting material. For this new edition, assistance from the following students and colleagues is grate- fully acknowledged: Behrooz Davazdahemami, Bhavana Baheti, Varnika Gottipati, and Chakradhar Pathi (all of Oklahoma State University). Prof. Rick Wilson contrib- uted some examples and new exercise questions for Chapter 8. Prof. Pankush Kalgotra (Auburn University) contributed the new streaming analytics tutorial in Chapter 9. Other contributors of materials for specific application stories are identified as sources in the respective sections. Susan Baskin, Imad Birouty, Sri Raghavan, and Yenny Yang of Tera- data provided special help in identifying new TUN content for the book and arranging permissions for the same.

Many other colleagues and students have assisted us in developing previous editions or the recent edition of the companion book from which some of the content has been adapted in this revision. Some of that content is still included this edition. Their assistance and contributions are acknowledged as well in chronological order. Dr. Dave Schrader contributed the sports examples used in Chapter 1. These will provide a great introduc- tion to analytics. We also thank INFORMS for their permission to highlight content from Interfaces. We also recognize the following individuals for their assistance in develop- ing Previous edition of the book: Pankush Kalgotra, Prasoon Mathur, Rupesh Agarwal, Shubham Singh, Nan Liang, Jacob Pearson, Kinsey Clemmer, and Evan Murlette (all of Oklahoma State University). Their help for BI 4e is gratefully acknowledged. The Tera- data Aster team, especially Mark Ott, provided the material for the opening vignette for Chapter 9. Dr. Brian LeClaire, CIO of Humana Corporation led with contributions of sev- eral real-life healthcare case studies developed by his team at Humana. Abhishek Rathi of vCreaTek contributed his vision of analytics in the retail industry. In addition, the follow- ing former PhD students and research colleagues of ours have provided content or advice and support for the book in many direct and indirect ways: Asil Oztekin, University of Massachusetts-Lowell; Enes Eryarsoy, Sehir University; Hamed Majidi Zolbanin, Ball State University; Amir Hassan Zadeh, Wright State University; Supavich (Fone) Pengnate, North Dakota State University; Christie Fuller, Boise State University; Daniel Asamoah, Wright State University; Selim Zaim, Istanbul Technical University; and Nihat Kasap, Sabanci Uni- versity. Peter Horner, editor of OR/MS Today, allowed us to summarize new application stories from OR/MS Today and Analytics Magazine. We also thank INFORMS for their permission to highlight content from Interfaces. Assistance from Natraj Ponna, Daniel Asamoah, Amir Hassan-Zadeh, Kartik Dasika, and Angie Jungermann (all of Oklahoma State University) is gratefully acknowledged for DSS 10th edition. We also acknowledge Jongswas Chongwatpol (NIDA, Thailand) for the material on SIMIO software, and Kazim Topuz (University of Tulsa) for his contributions to the Bayesian networks section in

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Chapter 5. For other previous editions, we acknowledge the contributions of Dave King (a technology consultant and former executive at JDA Software Group, Inc.) and Jerry Wagner (University of Nebraska–Omaha). Major contributors for earlier editions include Mike Goul (Arizona State University) and Leila A. Halawi (Bethune-Cookman College), who provided material for the chapter on data warehousing; Christy Cheung (Hong Kong Baptist University), who contributed to the chapter on knowledge management; Linda Lai (Macau Polytechnic University of China); Lou Frenzel, an independent consultant whose books Crash Course in Artificial Intelligence and Expert Systems and Understanding of Expert Systems (both published by Howard W. Sams, New York, 1987) provided material for the early editions; Larry Medsker (American University), who contributed substantial material on neural networks; and Richard V. McCarthy (Quinnipiac University), who per- formed major revisions in the seventh edition.

Previous editions of the book have also benefited greatly from the efforts of many individuals who contributed advice and interesting material (such as problems), gave feedback on material, or helped with class testing. These include Warren Briggs (Suffolk University), Frank DeBalough (University of Southern California), Mei-Ting Cheung (Uni- versity of Hong Kong), Alan Dennis (Indiana University), George Easton (San Diego State University), Janet Fisher (California State University, Los Angeles), David Friend (Pilot Soft- ware, Inc.), the late Paul Gray (Claremont Graduate School), Mike Henry (OSU), Dustin Huntington (Exsys, Inc.), Subramanian Rama Iyer (Oklahoma State University), Elena Karahanna (The University of Georgia), Mike McAulliffe (The University of Georgia), Chad Peterson (The University of Georgia), Neil Rabjohn (York University), Jim Ragusa (University of Central Florida), Alan Rowe (University of Southern California), Steve Ruth (George Mason University), Linus Schrage (University of Chicago), Antonie Stam (University of Missouri), Late Ron Swift (NCR Corp.), Merril Warkentin (then at Northeastern Uni- versity), Paul Watkins (The University of Southern California), Ben Mortagy (Claremont Graduate School of Management), Dan Walsh (Bellcore), Richard Watson (The University of Georgia), and the many other instructors and students who have provided feedback.

Several vendors cooperated by providing development and/or demonstration software: Dan Fylstra of Frontline Systems, Gregory Piatetsky-Shapiro of KDNuggets.com, Logic Programming Associates (UK), Gary Lynn of NeuroDimension Inc. (Gainesville, Florida), Palisade Software (Newfield, New York), Jerry Wagner of Planners Lab (Omaha, Nebraska), Promised Land Technologies (New Haven, Connecticut), Salford Systems (La Jolla, Califor- nia), Gary Miner of StatSoft, Inc. (Tulsa, Oklahoma), Ward Systems Group, Inc. (Frederick, Maryland), Idea Fisher Systems, Inc. (Irving, California), and Wordtech Systems (Orinda, California).

Special thanks to the Teradata University Network and especially to Hugh Watson, Michael Goul, and Susan Baskin, Program Director, for their encouragement to tie this book with TUN and for providing useful material for the book.

Many individuals helped us with administrative matters and editing, proofreading, and preparation. The project began with Jack Repcheck (a former Macmillan editor), who initiated this project with the support of Hank Lucas (New York University). Jon Outland assisted with the supplements.