Week 3 Assignment 3 Business Intelligence
Chapter 5
1. What is an artificial neural network and for what types
of problems can it be used?
2. Compare artificial and biological neural networks. What
aspects of biological networks are not mimicked by artificial
ones? What aspects are similar?
3. What are the most common ANN architectures? For
what types of problems can they be used?
4. ANN can be used for both supervised and unsupervised
learning. Explain how they learn in a supervised mode
and in an unsupervised mode.
Go to Google Scholar (scholar.google.com). Conduct
a search to find two papers written in the last five years
that compare and contrast multiple machine-learning
methods for a given problem domain. Observe commonalities
and differences among their findings and
prepare a report to summarize your understanding.
Go to neuroshell.com. Look at Gee Whiz examples.
Comment on the feasibility of achieving the results
claimed by the developers of this neural network model.
Chapter 6
What is deep learning? What can deep learning do that
traditional machine-learning methods cannot?
2. List and briefly explain different learning paradigms/
methods in AI.
3. What is representation learning, and how does it relate
to machine learning and deep learning?
4. List and briefly describe the most commonly used ANN
activation functions.
5. What is MLP, and how does it work? Explain the function
of summation and activation weights in MLP-type ANN.
Cognitive computing has become a popular term to define
and characterize the extent of the ability of machines/
computers to show intelligent behavior. Thanks to IBM
Chapter 6 Deep Learning and Cognitive Computing 385
Watson and its success on Jeopardy!, cognitive computing
and cognitive analytics are now part of many realworld
intelligent systems. In this exercise, identify at least
three application cases where cognitive computing was
used to solve complex real-world problems. Summarize
ANALYTICS, DATA SCIENCE, &
ARTIFICIAL INTELLIGENCE
SYSTEMS FOR DECISION SUPPORT
E L E V E N T H E D I T I O N
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
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Library of Congress Cataloging-in-Publication Data
Library of Congress Cataloging in Publication Control Number: 2018051774
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, andLocation Analytics:
Conceptsand 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
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
Simons 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
Contents v
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 AnalyticsAn Exciting Frontier for Learning and Understanding
Applications of Analytics 38
Analytics Applications in HealthcareHumana Examples 43
0 APPLICATION CASE 1.7 Image Analysis Helps Estimate Plant Cover 50
1.7 Artificial Intelligence Overview 52
What Is Artificial Intelligence? 52
The Major Benefits of AI 52
The Landscape of AI 52
0 APPLICATION CASE 1.8 AI Increases Passengers Comfort and
Security in Airports and Borders 54
The Three Flavors of AI Decisions 55
Autonomous AI 55
Societal Impacts 56
0 APPLICATION CASE 1.9 Robots Took the Job of Camel-Racing Jockeys
for Societal Benefits 58
1.8 Convergence of Analytics and AI 59
Major Differences between Analytics and AI 59
Why Combine Intelligent Systems? 60
How Convergence Can Help? 60
Big Data Is Empowering AI Technologies 60
The Convergence of AI and the IoT 61
The Convergence with Blockchain and Other Technologies 62
0 APPLICATION CASE 1.10 Amazon Go Is Open for Business 62
IBM and Microsoft Support for Intelligent Systems Convergence 63
1.9 Overview of the Analytics Ecosystem 63
1.10 Plan of the Book 65
1.11 Resources, Links, and the Teradata University Network
Connection 66
Resources and Links 66
Vendors, Products, and Demos 66
Periodicals 67
The Teradata University Network Connection 67
vi Contents
The Books 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 Googles Machine-Learning Tools 97
Conclusion 98
Contents vii
2.6 AI Applications in Accounting 99
AI in Accounting: An Overview 99
AI in Big Accounting Companies 100
Accounting Applications in Small Firms 100
0 APPLICATION CASE 2.4 How EY, Deloitte, and PwC Are Using AI 100
Job of Accountants 101
2.7 AI Applications in Financial Services 101
AI Activities in Financial Services 101
AI in Banking: An Overview 101
Illustrative AI Applications in Banking 102
Insurance Services 103
0 APPLICATION CASE 2.5 US Bank Customer Recognition and
Services 104
2.8 AI in Human Resource Management (HRM) 105
AI in HRM: An Overview 105
AI in Onboarding 105
0 APPLICATION CASE 2.6 How Alexander Mann Solutions (AMS) Is
Using AI to Support the Recruiting Process 106
Introducing AI to HRM Operations 106
2.9 AI in Marketing, Advertising, and CRM 107
Overview of Major Applications 107
AI Marketing Assistants in Action 108
Customer Experiences and CRM 108
0 APPLICATION CASE 2.7 Kraft Foods Uses AI for Marketing
and CRM 109
Other Uses of AI in Marketing 110
2.10 AI Applications in Production-Operation
Management (POM) 110
AI in Manufacturing 110
Implementation Model 111
Intelligent Factories 111
Logistics and Transportation 112
Chapter Highlights 112 Key Terms 113
Questions for Discussion 113 Exercises 114
References 114
Chapter 3 Nature of Data, Statistical Modeling, and Visualization 117
3.1 Opening Vignette: SiriusXM Attracts and Engages a
New Generation of Radio Consumers with Data-Driven
Marketing 118
3.2 Nature of Data 121
3.3 Simple Taxonomy of Data 125
0 APPLICATION CASE 3.1 Verizon Answers the Call for Innovation: The
Nations Largest Network Provider uses Advanced Analytics to Bring
the Future to its Customers 127
viii Contents
3.4 Art and Science of Data Preprocessing 129
0 APPLICATION CASE 3.2 Improving Student Retention with
Data-Driven Analytics 133
3.5 Statistical Modeling for Business Analytics 139
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
Contents ix
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
x Contents
Step 6: Deployment 217
Other Data Mining Standardized Processes and Methodologies 217
4.5 Data Mining Methods 220
Classification 220
Estimating the True Accuracy of Classification Models 221
Estimating the Relative Importance of Predictor Variables 224
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 Peoples
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 PatternsThe
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
Kohonens 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
Contents xi
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 Nave Bayes Method for Classification 278
Bayes Theorem 279
Nave Bayes Classifier 279
Process of Developing a Nave Bayes Classifier 280
Testing Phase 281
0 APPLICATION CASE 5.5 Predicting Disease Progress in Crohns
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
MotivationWhy 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
SummaryEnsembles are not Perfect! 301
0 APPLICATION CASE 5.6 To Imprison or Not to Imprison:
A Predictive Analytics-Based Decision Support System for
Drug Courts 304
Chapter Highlights 306 Key Terms 308
Questions for Discussion 308 Exercises 309
Internet Exercises 312 References 313
Chapter 6 Deep Learning and Cognitive Computing 315
6.1 Opening Vignette: Fighting Fraud with Deep Learning
and Artificial Intelligence 316
6.2 Introduction to Deep Learning 320
0 APPLICATION CASE 6.1 Finding the Next Football Star with
Artificial Intelligence 323
6.3 Basics of Shallow Neural Networks 325
0 APPLICATION CASE 6.2 Gaming Companies Use Data Analytics to
Score Points with Players 328
0 APPLICATION CASE 6.3 Artificial Intelligence Helps Protect Animals
from Extinction 333
xii Contents
6.4 Process of Developing Neural NetworkBased
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
Contents xiii
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 Fans
Experience 408
7.5 Text Mining Process 410
Task 1: Establish the Corpus 410
Task 2: Create the TermDocument 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
xiv Contents
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 Titos 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
Contents xv
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
xvi Contents
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, andLocation Analytics:
Conceptsand 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 Vs 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 eBays 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
Contents xvii
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
FluActivity 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
xviii Contents
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
