Transcription
ELEVENTH EDITIONANALYTICS,DATA SCIENCE,ARTIFICIALINTELLIGENCESYSTEMS FOR DECISION SUPPORTRamesh ShardaOklahoma State UniversityDursun DelenOklahoma State UniversityEfraim TurbanUniversity of Hawaii Pearson&
Preface xxvAbout the Authors xxxivIntroduction to Analytics and AI1Chapter 1 Overview of Business Intelligence, Analytics, DataScience, and Artificial Intelligence: Systems for DecisionSupport 21.1Openirig Vignette: How Intelligent Systems Work forKONE Elevators and Escalators Company 31.2Changing Business Environments and Evolving Needs forDecision Support and Analytics 5Decision-Making Process 6The Influence of the External and Internal Environments on the Process 6Data and Its Analysis in Decision Making 7Technologies for Data Analysis and Decision Support 71.3Decision-Making Processes and Computerized DecisionSupport Framework 9Simon's Process: Intelligence, Design, and Choice 9The Intelligence Phase: Problem (or Opportunity) Identification 10 APPLICATION CASE 1.1 Making Elevators Go Faster! 11The Design Phase 12The Choice Phase 13The Implementation Phase 13The Classical Decision Support System Framework 14A DSS Application 16Components of a Decision Support System 18The Data Management Subsystem 18The Model Management Subsystem 19 APPLICATION CASE 1.2 SNAP DSS Helps OneNet MakeTelecommunications Rate Decisions 201.4The User Interface Subsystem 20The Knowledge-Based Management Subsystem 21Evolution of Computerized Decision Support to BusinessIntelligence/Analytics/Data Science 22A Framework for Business Intelligence 25The Architecture of Bl 25The Origins and Drivers of Bl 26Data Warehouse as a Foundation for Business Intelligence 27Transaction Processing versus Analytic Processing 27A Multimedia Exercise in Business Intelligence 28
1.51.61.71.8Analytics Overview 30Descriptive Analytics 32 APPLICATION CASE 1.3 Silvaris Increases Business with VisualAnalysis and Real-Time Reporting Capabilities 32 APPLICATION CASE 1.4 Siemens Reduces Cost with the Use of DataVisualization 33Predictive Analytics 33 APPLICATION CASE 1.5 Analyzing Athletic injuries 34Prescriptive Analytics 34 APPLICATION CASE 1.6 A Specialty Steel Bar Company Uses Analyticsto Determine Available-to-Promise Dates 35Analytics Examples in Selected Domains 38Sports Analytics—An Exciting Frontier for Learning and UnderstandingApplications of Analytics 38Analytics Applications in Healthcare—Humana Examples 43 APPLICATION CASE 1.7 Image Analysis Helps Estimate Plant Cover 50Artificial Intelligence Overview 52What Is Artificial Intelligence? 52The Major Benefits of AI 52The Landscape of AI 52» APPLICATION CASE 1.8 AI Increases Passengers' Comfort andSecurity in Airports and Borders 54The Three Flavors of AI Decisions 55AutonomousAI 55Societal Impacts 56» APPLICATION CASE 1.9 Robots Took the Job of Camel-Racing Jockeysfor Societal Benefits 58Convergence of Analytics and AI 59Major Differences between Analytics and AI 59Why Combine Intelligent Systems? 60How Convergence Can Help? 60Big Data Is Empowering AI Technologies 60The Convergence of AI and the loT 611.9The Convergence with Blockchain and Other Technologies 62 APPLICATION CASE 1.10 Amazon Go Is Open for Business 62IBM and Microsoft Support for Intelligent Systems Convergence 63Overview of the Analytics Ecosystem 631.10 Plan of the Book 651.11 Resources, Links, and the Teradata University NetworkConnection 66Resources and Links 66Vendors, Products, and Demos 66Periodicais 67The Teradata University Network Connection67
vi ContentsThe Book's Web Site 67Chapter Highlights 67 Key Terms 68Questions for Discussion 68 Exercises 69References 70Chapter 2 Artificial Intelligence: Concepts, Drivers, MajorTechnologies, and Business Applications 732.1Opening Vignette: INRIX Solves TransportationProblems 742.2Introduction to Artificial IntelligenceDefinitions 76Major Characteristics of AI MachinesMajor Elements of AI 777677AI Applications 78Major Goals of AI 78Drivers of AI 79Benefits of AI 79Some Limitations of AI Machines 81Three Flavors of AI Decisions 812.3Artificial Brain 82Human and Computer Intelligence 83What Is Intelligence? 83How Intelligent Is AI? 842.4Measuring AI 85» APPLICATION CASE 2.1 How Smart Can a Vacuum Cleaner Be? 86Major AI Technologies and Some Derivatives87Intelligent Agents 87Machine Learning 88 APPLICATION CASE 2.2 How Machine Learning Is Improving Workin Business 89Machine and Computer Vision 90Robotic Systems 91Natural Language Processing 92Knowledge and Expert Systems and Recommenders 932.5Chatbots 94Emerging AI Technologies 94AI Support for Decision Making 95Some Issues and Factors in Using AI in Decision Making 96AI Support of the Decision-Making Process 96Automated Decision Making 97» APPLICATION CASE 2.3 How Companies Solve Real-World ProblemsUsing Google s Machine-Learning Tools 97Condusion 98
Contents vii2.6AI Applications in AccountingAI in Accounting: An Overview 99AI in Big Accounting Companies 10099Accounting Applications in Small Firms 100 APPLICATION CASE 2.4 How EY, Deloitte, and PwC Are Using AI 100Jobof Accountants 1012.7AI Applications in Financial Services 101AI Activities in Financial Services 101AI in Banking: An Overview 101Illustrative AI Applications in Banking 102Insurance Services 103 APPLICATION CASE 2.5 US Bank Customer Recognition andServices 1042.82.9AI in Human Resource Management (HRM) 105AI in HRM: An Overview 105AI in Onboarding 105 APPLICATION CASE 2.6 How Alexander Mann Solutions (AMS) IsUsing AI to Support the Recruiting Process 106Introducing AI to HRM Operations 106AI in Marketing, Advertising, and CRMOverview of Major Applications 107107AI Marketing Assistants in Action 108Customer Experiences and CRM 108 APPLICATION CASE 2.7 Kraft Foods Uses AI for Marketingand CRM 109Other Uses of AI in Marketing 1102.10 AI Applications in Production-OperationManagement (POM) 110AI in Manufacturing 110Implementation Model 111Intelligent Factories 111Logistics and Transportation 112Chapter Highlights 112 Key Terms 113Questions for Discussion 113 Exercises 114References 114Chapter 3 Nature of Data, Statistical Modeling, and Visualization 1173.1 Opening Vignette: SiriusXM Attracts and Engages aNew Generation of Radio Consumers with Data-DrivenMarketing 1183.2 Nature of Data 1213.3Simple Taxonomy of Data 125 APPLICATION CASE 3.1 Verizon Answers the Call for Innovation: TheNation's Largest Network Provider uses Advanced Analytics to Bringthe Future to its Customers 127
3.4Art and Science of Data Preprocessing 129» APPLICATION CASE 3.2 Improving Student Retention withData-Driven Analytics 1333.5Statistical Modeling for Business AnalyticsDescriptive Statistics for Descriptive Analytics 140139Measures of Centrality Tendency (Also Called Measures of Location orCentrality) 140Arithmetic Mean 140Median 141Mode 141Measures of Dispersion (Also Called Measures ofSpreadorDecentrality) 142Range 142Variance 142Standard Deviation 143Mean Absolute Deviation 143Quartiles and Interquartile Range 143Box-and-Whiskers Plot 1433.63.73.83.9Shape of a Distribution 145 APPLICATION CASE 3.3 Town of Cary Uses Analytics to Analyze Dataf rom Sensors. Assess Demand, and Detect Problems 150Regression Modeling for Inferential Statistics 151How Do We Develop the Linear Regression Model? 152How Do We Know If the Model Is Good Enough? 153What Are the Most Important Assumptions in Linear Regression? 154Logistic Regression 155Time-Series Forecasting 156 APPLICATION CASE 3.4 Predicting NCAA Bowl GameOutcomes 157Business Reporting 163 APPLICATION CASE 3.5 Flood of Paper Ends at FEMA 165Data Visualization 166Brief Histoiy of Data Visualization 167» APPLICATION CASE 3.6 Macfarlan Smith Improves OperationalPerformance Insight with Tableau Online 169Different Types of Charts and Graphs 171Basic Charts and Graphs 171Specialized Charts and Graphs 172Which Chart or Graph Should You Use? 1743.10 Emergence of Visual AnalyticsVisual Analytics 178176High-Powered Visual Analytics Environments 1803.11 Information Dashboards182
Contents ix» APPLICATION CASE 3.7 Dallas Cowboys Score Big with Tableauand Teknion 184Dashboard Design 184 APPLICATION CASE 3.8 Visual Analytics Helps Energy Supplier MakeBetter Connections 185What to Look for in a Dashboard 186Best Practices in Dashboard Design 187Benchmark Key Performance Indicators with Industry Standards 187Wrap the Dashboard Metrics with Contextual Metadata 187Validate the Dashboard Design by a Usability Specialist 187Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard 188Enrich the Dashboard with Business-User Comments 188Present Information in Three Different Levels 188Pick the Right Visual Construct Using Dashboard Design Principles 188Provide for Guided Analytics 188Chapter Highlights 188 Key Terms 189Questions for Discussion 190 Exercises 190References 192PART IIPredictive Analytics/Machine Learning193Chapter 4 Data Mining Process, Methods, and Algorithms 1944.14.24.34.4Opening Vignette: Miami-Dade Police Department Is UsingPredictive Analytics to Foresee and Fight Crime 195Data Mining Concepts 198» APPLICATION CASE 4.1 Visa Is Enhandng the CustomerExperience while Reducing Fraud with Predictive Analyticsand Data Mining 199Definitions, Characteristics, and Benefits 201How Data Mining Works 202 APPLICATION CASE 4.2 American Honda Uses Advanced Analytics toImprove Warranty Claims 203Data Mining Versus Statistics 208Data Mining Applications 208 APPLICATION CASE 4.3 Predictive Analytic and Data Mining HelpStop Terrorist Funding 210Data Mining Process211Step 1: Business Understanding 212Step 2: Data Understanding 212Step 3: Data Preparation 213Step 4: Model Building 214 APPLICATION CASE 4.4 Data Mining Helps inCancer Research 214Step 5: Testing and Evaluation 217
Step 6: Deployment 217Other Data Mining Standardized Processes and Methodologie: 217Data Mining MethodsClassification 220220Estimating the True Accuracy of Classification Models221Estimating the Relative Importance of Predictor Variables 224Cluster Analysis for Data Mining 228 APPLICATION CASE 4.5 Influence Health Uses Advanced PredictiveAnalytics to Focus on the Factors That Really Influence People'sHealthcare Decisions 2294.64.7Association Rule Mining 232Data Mining Software Tools236 APPLICATION CASE 4.6 Data Mining goes to Hollywood: PredictingFinancial Success of Movies 239Data Mining Privacy Issues, Myths, and Blunders 242 APPLICATION CASE 4.7 Predicting Customer Buying Patterns—TheTarget Story 243Data Mining Myths and Blunders 244Chapter Highlights 246 Key Terms 247Questions for Discussion 247 Exercises 248References 250Chapter 5 Machine-Learning Techniques for PredictiveAnalytics 2515.1 Opening Vignette: Predictive Modeling HelpsBetter Understand and Manage Complex MedicalProcedures 2525.2 Basic Concepts of Neural Networks 255Biological versus Artificial Neural Networks 256» APPLICATION CASE 5.1 Neural Networks are Helping to SaveLives in the Mining Industry 2585.3 Neural Network Architectures 2595.4Kohonen's Self-Organizing Feature Maps 259Hopfield Networks 260 APPLICATION CASE 5.2 Predictive Modeling Is Powering the PowerGenerators 261Support Vector Machines 263 APPLICATION CASE 5.3 Identifying Injury Severity Risk Factors inVehicle Crashes wlth Predictive Analytics 264Mathematical Formulation of SVMPrimat Form 269Dual Form 269Soft Margin 270Nonlinear Classification 270Kemel Trick 271269
Contents xi5.5Process-Based Approach to the Use of SVM5.6Support Vector Machines versus Artificial Neural Networks 273Nearest Neighbor Method for Prediction 2745.75.85.9271Similarity Measure: The Distance Metrie 275Parameter Selection 275 APPLICATION CASE 5.4 Efficient Image Recognition andCategorization with *nn 277Naive Bayes Method for Classification 278Bayes Theorem 279Naive Bayes Classifier 279Process of Developing a Naive Bayes Classifier 280Testing Phase 281 APPLICATION CASE 5.5 Predicting Disease Progress in Crohn'sDisease Patients: A Comparison of Analytics Methods 282Bayesian Networks 287How Does BN Work? 287How Can BN Be Constructed? 288Ensemble Modeling293Motivation—Why Do We Need to Use Ensembles? 293Different Types of Ensembles 295Bagging 296Boosting 298Variants of Bagging and BoostingStacking 300299Information Fusion 300Summary—Ensembles are not Perfect! 301» APPLICATION CASE 5.6 To Imprison or Not to Imprison:A Predictive Analytics-Based Decision Support System forDrug Courts 304Chapter Highlights 306 Key Terms 308Questions for Discussion 308 Exercises 309Internet Exercises 312 References 313Chapter 6 Deep Learning and Cognitive Computing3156.1Opening Vignette: Fighting Fraud with Deep Learningand Artificial Intelligence 3166.2Introduction to Deep Learning 320 APPLICATION CASE 6.1 Finding the Next Football Star withArtificial Intelligence 323Basics of "Shallow" Neural Networks 325 APPLICATION CASE 6.2 Gaming Companies Use Data Analytics toScore Points with Players 328 APPLICATION CASE 6.3 Artificial Intelligence Helps Protect Animalsfrom Extinction 3336.3
xii Contents6.4Process of Developing Neural Network-BasedSystems 334Learning Process in ANN 335Backpropagation for ANN Training 3366.5llluminating the Black Box of ANN 340 APPLICATION CASE 6.4 Sensitivity Analysis Reveals Injury SeverityFactors in Traffic Accidents 3416.6Deep Neural Networks 343Feedforward Multilayer Perceptron (MLP)-Type Deep Networks 343Impact of Random Weights in Deep MLP 344More Hidden Layers versus More Neurons? 345 APPLICATION CASE 6.5 Georgia DOT Variable Speed Limit AnalyticsHelp Solve Traffic Congestions 3466.7Convolutional Neural Networks 349Convolution Function 349Pooling 352Image Processing Using Convolutional Networks 353» APPLICATION CASE 6.6 From Image Recognition to FaceRecognition 356Text Processing Using Convolutional Networks 3576.86.9Recurrent Networks and Long Short-Term MemoryNetworks 360 APPLICATION CASE 6.7 Deliver Innovation by UnderstandingCustomer Sentiments 363LSTM Networks Applications 365Computer Frameworks for Implementation of DeepLearning 368Torch 368Caffe 368TensorFlow 369Theano 369Keras: An Application Programming Interface 3706.10Cognitive Computing 370How Does Cognitive Computing Work? 371How Does Cognitive Computing Differ from AI?372Cognitive Search 374IBM Watson; Analytics atlts Best 375 APPLICATION CASE 6.8 IBM Watson Competes against theBest at Jeopardyl 376How Does Watson Do It? 377Whatls the Future for Watson? 377Chapter Highlights 381 Key Terms 383Questions for Discussion 383 Exercises 384References 385
Chapter 7 Text Mining, Sentiment Analysis, and Social Analytics 3887.1 Opening Vignette: Amadori Group Converts ConsumerSentiments into Near-Real-Time Sales 3897.2 Text Analytics and Text Mining Overview 392» APPLICATION CASE 7.1 Netflix: Using Big Data to Drive BigEngagement: Unlocking the Power of Analytics to DriveContent and Consumer Insight 3957.3 Natural Language Processing (NLP) 397 APPLICATION CASE 7.2 AMC Networks Is Using Analytics toCapture New Viewers, Predict Ratings, and Add Value for Advertisersin a Multichannel World 3997.4 Text Mining Applications 402Marketing Applications 403Security Applications 4037.5Biomedical Applications 404 APPLICATION CASE 7.3 Mining for Lies 404Academic Applications 407 APPLICATION CASE 7.4 The Magic Behind the Magic: Instant Accessto Information Helps the Orlando Magic Up their Game and the Fan'sExperience 408Text Mining Process 410Task 1: Establish the Corpus 410Task 2: Create the Term-Document Matrix 411Task 3: Extract the Knowledge 413 APPLICATION CASE 7.5 Research Literature Survey with TextMining 4157.6Sentiment Analysis418» APPLICATION CASE 7.6 Creating a Unique Digital Experience toCapture Moments That Matter at Wimbledon 419Sentiment Analysis Applications 422Sentiment Analysis Process 424Methods for Polarity Identification 426Using a Lexicon 426Using a Collection of Training Documents 427Identifying Semantic Orientation of Sentences and Phrases 4287.7Identifying Semantic Orientation of DocumentsWeb Mining Overview 4297.8Web Content and Web Structure MiningSearch Engines 433431Anatomy of a Search Engine 4341. Development Cyde 4342. Response Cyde 435Search Engine Optimization436Methods for Search Engine Optimization 437428
7.9 APPLICATION CASE 7.7 Delivering Individualized Content andDriving Digital Engagement: How Barbour Collected More Than 49,000New Leads in One Month with Teradata Interactive 439Web Usage Mining (Web Analytics)441Web Analytics Technologies 441Web Analytics Metrics 442Web Site Usability 442Traffic Sources 443Visitor Profiles 4447.10Conversion Statistics 444Social Analytics 446Social Network Analysis446Social Network Analysis Metrics 447 APPLICATION CASE 7.8 Tito's Vodka Establishes Brand Loyalty withan Authentic Social Strategy 447Connections 450Distributions 450Segmentation 451Social Media Analytics 451How Do People Use Social Media?452Measuring the Social Media Impact 453Best Practices in Social Media Analytics 453Chapter Highlights 455 Key Terms 456Questions for Discussion 456 Exercises 456References 457Prescriptive Analytics and Big Data459Chapter 8 Prescriptive Analytics: Optimization and Simulation 4608.1 Opening Vignette: School District of Philadelphia UsesPrescriptive Analytics to Find Optimal Solution forAwarding Bus Route Contracts 4618.2 Model-Based Decision Making 462 APPLICATION CASE 8.1 Canadian Football League Optimizes GameSchedule 463Prescriptive Analytics Model Examples 465Identification of the Problem and Environmental Analysis 465 APPLICATION CASE 8.2 Ingram Micro Uses Business IntelligenceApplications to Make Pricing Decisions 4668.3Model Categories 467Structure of Mathematical Models for DecisionSupport 469The Components of Decision Support Mathematical Models 469The Structure of Mathematical Models 470
Contents xv8.48.58.68.78.88.9Certainty, Uncertainty, and RiskDecision Making under Certainty 471471Decision Making under Uncertainty 472Decision Making under Risk (Risk Analysis) 472 APPLICATION CASE 8.3 American Airlines Uses Should-CostModeling to Assess the Uncertainty of Bids for ShipmentRoutes 472Decision Modeling with Spreadsheets 473 APPLICATION CASE 8.4 Pennsylvania Adoption Exchange UsesSpreadsheet Model to Better Match Children with Families 474 APPLICATION CASE 8.5 Metro Meals on Wheels Treasure Valley UsesExcel to Find Optimal Delivery Routes 475Mathematical Programming Optimization 477 APPLICATION CASE 8.6 Mixed-Integer Programming ModelHelps the University of Tennessee Medical Center with SchedulingPhysicians 478Linear Programming Model 479Modeling in LP: An Example 480Implementation 484Multiple Goals, Sensitivity Analysis, What-If Analysis, andGoal Seeking 486Multiple Goals 486Sensitivity Analysis 487What-If Analysis 488Goal Seeking 489Decision Analysis with Decision Tables and DecisionTrees 490Decision Tables 490Decision Trees 492Introduction to Simulation493Major Characteristics of Simulation 493 APPLICATION CASE 8.7 Steel Tubing Manufacturer Uses aSimulation-Based Production Scheduling System 493Advantages of Simulation 494Disadvantages of Simulation 495The Methodology of Simulation 495Simulation Types 496Monte Carlo Simulation 497Discrete Event Simulation 498 APPLICATION CASE 8.8 Cosan Improves Its Renewable EnergySupply Chain Using Simulation4988.10Visual Interactive Simulation 500Conventional Simulation Inadequacies 500Visual Interactive Simulation 500
xvi ContentsVisual Interactive Models and DSS 500Simulation Software 501 APPLICATION CASE 8.9 Improving Job-Shop Scheduling Decisionsthrough RFID: A Simulation-Based Assessment501Chapter Highlights 505 Key Terms 505Questioris for Discussion 505 Exercises 506References 508Chapter 9 Big Data, Cloud Computing, and Location Analytics:Concepts and Tools 5099.19.29.39.49.59.69.7Opening Vignette: Analyzing Customer Churn in a TelecomCompany Using Big Data Methods 510Definition of Big Data 513The "V"s That Define Big Data 514 APPLICATION CASE 9.1 Alternative Data for Market Analysis orForecasts 517Fundamentals of Big Data Analytics 519Business Problems Addressed by Big Data Analytics 521 APPLICATION CASE 9.2 Overstock.com Combines Multiple Datasetsto Understand Customer Journeys 522Big Data Technologies 523MapReduce 523Why Use MapReduce? 523Hadoop 524How Does Hadoop Work? 525Hadoop Technical Components 525Hadoop: The Pros and Cons 527NoSQL 528 APPLICATION CASE 9.3 eBay's Big Data Solution 529 APPLICATION CASE 9.4 Understanding Quality and Reliabilityof Healthcare Support Information on IWitter 531Big Data and Data Warehousing 532Use Cases for Hadoop 533Use Cases for Data Warehousing 534The Gray Areas (Any One of the Two Would Do the Job) 535Coexistence of Hadoop and Data Warehouse 536In-Memory Analytics and Apache Spark 537» APPLICATION CASE 9.5 Using Natural Language Processing toanalyze customer Feedback in TripAdvisor reviews 538Architecture of ApacheSpark 538Getting Started with Apache Spark 539Big Data and Stream Analytics 543Stream Analytics versus Perpetual Analytics 544Cr'rtical Event Processing 545Data Stream Mining 546Applications of Stream Analytics 546
Contents xviie-Commerce 546Telecommunications 546 APPLICATION GASE 9.6 Salesforce Is Using Streaming Data toEnhance Customer Value 547Law Enforcement and Cybersecurity 547Power Industry 548Financial Services 548Health Sciences 548Government 5489.89.9Big Data Vendors and Platforms 549Infrastructure Services Providers 550Analytics Solution Providers 550Business Intelligence Providers Incorporating Big Data 551 APPLICATION CASE 9.7 Using Social Media for NowcastingFlu Activity 551 APPLICATION CASE 9.8 Analyzing Disease Patterns from anElectronic Medical Records Data Warehouse 554Cloud Computing and Business Analytics 557Data as a Service (DaaS) 558Software as a Service (SaaS) 559Platform as a Service (PaaS) 559I nfrastructure as a Service (laaS) 559Essential Technologies for Cloud Computing 560 APPLICATION CASE 9.9 Major West Coast Utility Uses Cloud-MobileTechnology to Provide Real-Time Incident Reporting 561Cloud Deployment Models 563Major Cloud Platform Providers in Analytics563Analytics as a Service (AaaS) 564Representative Analytics as a Service Offerings 564Illustrative Analytics Applications Employing the Cloud Infrastructure 565Using Azure IOT, Stream Analytics, and Machine Learning to Improve MobileHealth Care Services 565Gulf Air Uses Big Data to Get Deeper Customer Insight 566Chime Enhances Customer Experience Using Snowflake 5669.10 Location-Based Analytics for Organizations 567Geospatial Analytics 567 APPLICATION CASE 9.10 Great Clips Employs Spatial Analytics toShave Time in Location Decisions 570 APPLICATION CASE 9.11 Starbucks Exploits GIS and Analytics toGrow Worldwide 570Real-Time Location Intelligence572Analytics Applications for Consumers 573Chapter Highlights 574 Key Terms 575Questions for Discussion 575 Exercises 575References 576
rgyrnj* Robotics, Social Networks, AI and loT579Chapter 10 Robotics: Industrial and Consumer Applications10.158010.2Opening Vignette: Robots Provide Emotional Supportto Patients and Children 581Overview of Robotics 58410.3History of Robotics 58410.4Illustrative Applications of Robotics 586Changing Precision Technology 586Adidas 586BMW Employs Collaborative Robots 587Tega587San Francisco Burger Eatery 588Spyce 588Mahindra & Mahindra Ltd. 589Robots in the Defense Industry 589Pepper 590Da Vinci Surgical System 592Snoo - A Robotic Crib 593MED! 59310.510.610.710.810.9Care-E Robot 593AGR0B0T 594Components of Robots595Various Categories of Robots 596Autonomous Cars: Robots in MotionAutonomous Vehide Development 598597Issues with Self-Driving Cars 599Impact of Robots on Current and Future Jobs 600Legal Implications of Robots and Artificial IntelligenceTort Liability 603Patents 603Property 604Taxation 604Practica of Law 604Constitutional Law 605Professional Certification 605Law Enforcement 605Chapter Highlights 606 Key Terms 606Questions for Discussion 606 Exercises 607References 607
Chapter 11 Group Decision Making, Collaborative Systems, andAI Support 61011.1 Opening Vignette: Hendrick Motorsports Excels withCollaborative Teams 61111.2 Making Decisions in Croups: Characteristics, Process,Benefits, and Dysfunctions 613Characteristics of Group Work 613Types of Decisions Made by Croups 614Group Decision-Making Process 614Benefits and Limitations of Group Work 61511.3 Supporting Group Work and Team Collaboration withComputerized Systems 616Overview of Group Support Systems (GSS) 61711.4Time/Place Framework 617Group Collaboration for Decision Support 618Electronic Support for Group Communication andCollaboration 619Groupware for Group Collaboration 619Synchronous versus Asynchronous Products 619Virtual Meeting Systems 620Collaborative Networks and Hubs 622Collaborative Hubs 622Social Collaboration 622Sample of Populär Collaboration Software 62311.5 Direct Computerized Support for Group DecisionMaking 623Group Decision Support Systems (GDSS) 624Characteristics of GDSS 625Supporting the Entire Decision-Making Process 625Brainstorming for Idea Generation and Problem Solving 627Group Support Systems 62811.6 Collective Intelligence and CollaborativeIntelligence 629Definitions and Benefits 629Computerized Support to Collective Intelligence 629» APPLICATION CASE 11.1 Collaborative Modeling for OptimalWater Management: The Oregon State UniversityProject 630How Collective Intelligence May Change Work and Life 631Collaborative Intelligence 632How to Create Business Value from Collaboration: The IBMStudy 632
11.7Crowdsourcing as a Method for Decision SupportThe Essentials of Crowdsourcing633633Crowdsourcing for Problem-Solving and Decision Support 634Implementing Crowdsourcing for Problem Solving 635 APPLICATION CASE 11.2 How InnoCentive Helped GSK Solve aDifficult Problem 63611.8 Artificial Intelligence and Swarm AI Support of TeamCollaboration and Group Decision Making 636AI Support of Group Decision Making 637AI Support of Team Collaboration 637Swarm Intelligence and Swarm AI 639 APPLICATION CASE 11.3 XPRIZE Optimizes Visioneering 63911.9Human-Machine Collaboration and Teams of Robots 640Human-Machine Collaboration in Cognitive Jobs 641Robots as Coworkers: Opportunities and Challenges 641Teams of collaborating Robots 642Chapter Highlights 644 Key Terms 645Questions for Discussion 645 Exercises 645References 646Chapter 12 Knowledge Systems: Expert Systems, Recommenders,Chatbots, Virtual Personal Assistants, and RoboAdvisors 64812.112.2Opening Vignette: Sephora Excels with Chatbots 649Expert Systems and Recommenders 650Basic Concepts of Expert Systems (ES) 650Characteristics and Benefits of ES 652Typical Areas for ES Applications 653Structure and Process of ES 653 APPLICATION CASE 12.1 ES Aid in Identification of Chemical,Biological, and Radiological Agents 655Why the Classical Type of ES Is Disappearing 655 APPLICATION CASE 12.2 VisiRule 656Recommendation Systems 657 APPLICATION CASE 12.3 Netflix Recommender: A Critical SuccessFactor 65812.3 Concepts, Drivers, and Benefits of Chatbots 660What Is a Chatbot? 660Chatbot Evolution 660Components of Chatbots and the Process of Their Ilse 662Drivers and Benefits 663Representative Chatbots from Around the World 66312.4Enterprise Chatbots 664The Interest of Enterprises in Chatbots 664
Enterprise Chatbots: Marketing and Customer Experience 665 APPLICATION CASE 12.4 WeChat's Super Chatbot 666» APPLICATION CASE 12.5 How Vera Gold Mark Uses Chatbots toIncrease Sales 667Enterprise Chatbots: Financial Services 668Enterprise Chatbots: Service Industries 668Chatbot Platforms 669» APPLICATION CASE 12.6 Transavia Airlines Uses Bots forCommunication and Customer Care Delivery 669Knowledge for Enterprise Chatbots 67112.5 Virtual Personal Assistants 672Assistant for Information Search 672If You Were Mark Zuckerberg, Facebook CEO 672Amazon's Alexa and Echo 672Apple's Siri 675Google Assistant 675Other Personal Assistants 675Competition Among Large Tech Companies 675Knowledge for Virtual Personal Assistants 67512.612.7Chatbots as Professional Advisors (Robo Advisors) 676Robo Financial Advisors 676Evolution of Financial Robo Advisors 676Robo Advisors 2.0: Adding the Human Touch 676 APPLICATION CASE 12.7 Betterment, the Pioneer of Financial RoboAdvisors 677Managing Mutual Funds Using AI 678Other Professional Advisors 678IBM Watson 680Implementation Issues 680Technology Issues 680Disadvantages and Limitations of BotsQualityof Chatbots 681681Setting Up Alexa's Smart Home System 682Constructing Bots 682Chapter Highlights 683 Key Terms 683Questions for Discussion 684 Exercises 684References 685Chapter 13 The Internet of Things as a Platform for IntelligentApplications 68713.1Opening Vignette: CNH Industrial Uses the Internet ofThings to Excel 68813.2Essentials of loT 689Definitions and Characteristics 690
xxii ContentsThe loT Ecosystem13.3691Structure of loT Systems 691Major Benefits and Drivers of loT 694Major Benefits of loT 694Major Drivers of loT 695Opportunities 69513.4HowloT Works 696loT and Decision Support 69613.5 Sensors and Their Role in loT 697Brief Introduction to Sensor Technology 697» APPLICATION CASE 13.1 Using Sensors, loT, and AI forEnvironmental Control at the Athens, Greece,International Airport 697How Sensors Work with loT 698 APPLICATION CASE 13.2 Rockwell AutomationMonitors Expensive Oil and Gas Exploration Assets toPredict Failures 698Sensor Applications and Radio-Frequency Identification (RFID) Sensors 69913.6Selected loT Applications70113.7A Large-scale loT in Action 701Examples of Other Existing Applications 701Smart Homes and Appliances 703Typical Components of Smart Homes 703Smart Appliances 704A Smart Home Is Where the Bot Is 706Barriersto Smart Home Adoption 70713.8 Smart Cities and Factories 707» APPLICATION CASE 13.3 Amsterdam on the Road to Become aSmart City 708Smart Buildings: From Automated to Cognitive Buildings 709Smart Components in Smart Cities and Smart Factories 709» APPLICATI
ARTIFICIAL INTELLIGENCE SYSTEMS FOR DECISION SUPPORT Ramesh Sharda Oklahoma State University Dursun Delen Oklahoma State University Efraim Turban University of Hawaii Pearson . Preface xxv About the Authors xxxiv Introduction to Analytics and AI 1 Chapter 1 Overview of Business Intelligence, Analytics, Data Science, and Artificial .
