Chapter 4 ? discussion question #1-5 & exercise 1. Note: For exercise 1,the link provided in the textbook is not working, use t

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

Microsoft and/or its respective suppliers make no representations about the suitability of the information contained in the documents and related graphics published as part of the services for any purpose. All such documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/or its respective suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection with the use or performance of information available from the services. The documents and related graphics contained herein could include technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s) described herein at any time. Partial screen shots may be viewed in full within the software version specified. Microsoft® Windows® and Microsoft Office® are registered trademarks of Microsoft Corporation in the U.S.A. and other countries. This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation.

Vice President of Courseware Portfolio Management: Andrew Gilfillan

Executive Portfolio Manager: Samantha Lewis Team Lead, Content Production: Laura Burgess Content Producer: Faraz Sharique Ali Portfolio Management Assistant: Bridget Daly Director of Product Marketing: Brad Parkins Director of Field Marketing: Jonathan Cottrell Product Marketing Manager: Heather Taylor Field Marketing Manager: Bob Nisbet Product Marketing Assistant: Liz Bennett Field Marketing Assistant: Derrica Moser Senior Operations Specialist: Diane Peirano Senior Art Director: Mary Seiner

Interior and Cover Design: Pearson CSC Cover Photo: Phonlamai Photo/Shutterstock Senior Product Model Manager: Eric Hakanson Manager, Digital Studio: Heather Darby Course Producer, MyLab MIS: Jaimie Noy Digital Studio Producer: Tanika Henderson Full-Service Project Manager: Gowthaman

Sadhanandham Full Service Vendor: Integra Software Service

Pvt. Ltd. Manufacturing Buyer: LSC Communications,

Maura Zaldivar-Garcia Text Printer/Bindery: LSC Communications Cover Printer: Phoenix Color

ISBN 10: 0-13-519201-3 ISBN 13: 978-0-13-519201-6

Copyright © 2020, 2015, 2011 by Pearson Education, Inc. 221 River Street, Hoboken, NJ 07030. All rights reserved. Manufactured in the United States of America. This publication is protected by Copyright, and permission should be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. For information regarding permissions, request forms and the appropriate contacts within the Pearson Education Global Rights & Permissions Department, please visit www.pearsoned.com/permissions. Acknowledgments of third-party content appear on the appropriate page within the text, which constitutes an extension of this copyright page. Unless otherwise indicated herein, any third-party trademarks that may appear in this work are the property of their respective owners and any references to third-party trademarks, logos or other trade dress are for demonstrative or descriptive purposes only. Such references are not intended to imply any sponsorship, endorsement, authorization, or promotion of Pearson’s products by the owners of such marks, or any relationship between the owner and Pearson Education, Inc. or its affiliates, authors, licensees or distributors.

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, 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

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

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 Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics 38

Analytics Applications in Healthcare—Humana Examples 43 0 APPLICATION CASE 1.7 Image Analysis Helps Estimate Plant Cover 50

1.7 Artificial Intelligence Overview 52

What Is Artificial Intelligence? 52

The Major Benefits of AI 52

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

Security in Airports and Borders 54

The Three Flavors of AI Decisions 55

Autonomous AI 55

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

for Societal Benefits 58

1.8 Convergence of Analytics and AI 59

Major Differences between Analytics and AI 59

Why Combine Intelligent Systems? 60

How Convergence Can Help? 60

Big Data Is Empowering AI Technologies 60

The Convergence of AI and the IoT 61

The Convergence with Blockchain and Other Technologies 62 0 APPLICATION CASE 1.10 Amazon Go Is Open for Business 62

IBM and Microsoft Support for Intelligent Systems Convergence 63

1.9 Overview of the Analytics Ecosystem 63

1.10 Plan of the Book 65

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

Resources and Links 66

Vendors, Products, and Demos 66

Periodicals 67

The Teradata University Network Connection 67

vi Contents

The Book’s Web Site 67 Chapter Highlights 67 • Key Terms 68

Questions for Discussion 68 • Exercises 69

References 70

Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications 73 2.1 Opening Vignette: INRIX Solves Transportation

Problems 74

2.2 Introduction to Artificial Intelligence 76

Definitions 76

Major Characteristics of AI Machines 77

Major Elements of AI 77

AI Applications 78

Major Goals of AI 78

Drivers of AI 79

Benefits of AI 79

Some Limitations of AI Machines 81

Three Flavors of AI Decisions 81

Artificial Brain 82

2.3 Human and Computer Intelligence 83

What Is Intelligence? 83

How Intelligent Is AI? 84

Measuring AI 85 0 APPLICATION CASE 2.1 How Smart Can a Vacuum Cleaner Be? 86

2.4 Major AI Technologies and Some Derivatives 87

Intelligent Agents 87

Machine Learning 88 0 APPLICATION CASE 2.2 How Machine Learning Is Improving Work

in Business 89

Machine and Computer Vision 90

Robotic Systems 91

Natural Language Processing 92

Knowledge and Expert Systems and Recommenders 93

Chatbots 94

Emerging AI Technologies 94

2.5 AI Support for Decision Making 95

Some Issues and Factors in Using AI in Decision Making 96

AI Support of the Decision-Making Process 96

Automated Decision Making 97 0 APPLICATION CASE 2.3 How Companies Solve Real-World Problems

Using Google’s Machine-Learning Tools 97

Conclusion 98

Contents vii

2.6 AI Applications in Accounting 99

AI in Accounting: An Overview 99

AI in Big Accounting Companies 100

Accounting Applications in Small Firms 100 0 APPLICATION CASE 2.4 How EY, Deloitte, and PwC Are Using AI 100

Job of Accountants 101

2.7 AI Applications in Financial Services 101

AI Activities in Financial Services 101

AI in Banking: An Overview 101

Illustrative AI Applications in Banking 102

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

Services 104

2.8 AI in Human Resource Management (HRM) 105

AI in HRM: An Overview 105

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

Using AI to Support the Recruiting Process 106

Introducing AI to HRM Operations 106

2.9 AI in Marketing, Advertising, and CRM 107

Overview of Major Applications 107

AI Marketing Assistants in Action 108

Customer Experiences and CRM 108 0 APPLICATION CASE 2.7 Kraft Foods Uses AI for Marketing

and CRM 109

Other Uses of AI in Marketing 110

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

AI in Manufacturing 110

Implementation Model 111

Intelligent Factories 111

Logistics and Transportation 112 Chapter Highlights 112 • Key Terms 113

Questions for Discussion 113 • Exercises 114

References 114

Chapter 3 Nature of Data, Statistical Modeling, and Visualization 117 3.1 Opening Vignette: SiriusXM Attracts and Engages a

New Generation of Radio Consumers with Data-Driven Marketing 118

3.2 Nature of Data 121

3.3 Simple Taxonomy of Data 125 0 APPLICATION CASE 3.1 Verizon Answers the Call for Innovation: The

Nation’s Largest Network Provider uses Advanced Analytics to Bring the Future to its Customers 127

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 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

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 Naïve Bayes Method for Classification 278

Bayes Theorem 279

Naïve Bayes Classifier 279

Process of Developing a Naïve Bayes Classifier 280

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

Disease Patients: A Comparison of Analytics Methods 282

5.8 Bayesian Networks 287

How Does BN Work? 287

How Can BN Be Constructed? 288

5.9 Ensemble Modeling 293

Motivation—Why Do We Need to Use Ensembles? 293

Different Types of Ensembles 295

Bagging 296

Boosting 298

Variants of Bagging and Boosting 299

Stacking 300

Information Fusion 300

Summary—Ensembles are not Perfect! 301 0 APPLICATION CASE 5.6 To Imprison or Not to Imprison:

A Predictive Analytics-Based Decision Support System for Drug Courts 304

Chapter Highlights 306 • Key Terms 308

Questions for Discussion 308 • Exercises 309

Internet Exercises 312 • References 313

Chapter 6 Deep Learning and Cognitive Computing 315 6.1 Opening Vignette: Fighting Fraud with Deep Learning

and Artificial Intelligence 316

6.2 Introduction to Deep Learning 320 0 APPLICATION CASE 6.1 Finding the Next Football Star with

Artificial Intelligence 323

6.3 Basics of “Shallow” Neural Networks 325 0 APPLICATION CASE 6.2 Gaming Companies Use Data Analytics to

Score Points with Players 328

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

xii Contents

6.4 Process of Developing Neural Network–Based Systems 334

Learning Process in ANN 335

Backpropagation for ANN Training 336

6.5 Illuminating the Black Box of ANN 340 0 APPLICATION CASE 6.4 Sensitivity Analysis Reveals Injury Severity

Factors in Traffic Accidents 341

6.6 Deep Neural Networks 343

Feedforward Multilayer Perceptron (MLP)-Type Deep Networks 343

Impact of Random Weights in Deep MLP 344

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

Help Solve Traffic Congestions 346

6.7 Convolutional Neural Networks 349

Convolution Function 349

Pooling 352

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

Recognition 356

Text Processing Using Convolutional Networks 357

6.8 Recurrent Networks and Long Short-Term Memory Networks 360 0 APPLICATION CASE 6.7 Deliver Innovation by Understanding

Customer Sentiments 363

LSTM Networks Applications 365

6.9 Computer Frameworks for Implementation of Deep Learning 368

Torch 368

Caffe 368

TensorFlow 369

Theano 369

Keras: An Application Programming Interface 370

6.10 Cognitive Computing 370

How Does Cognitive Computing Work? 371

How Does Cognitive Computing Differ from AI? 372

Cognitive Search 374

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

Best at Jeopardy! 376

How Does Watson Do It? 377

What Is the Future for Watson? 377 Chapter Highlights 381 • Key Terms 383

Questions for Discussion 383 • Exercises 384

References 385

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 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

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 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

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, 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

<

Collepals.com Plagiarism Free Papers

Are you looking for custom essay writing service or even dissertation writing services? Just request for our write my paper service, and we'll match you with the best essay writer in your subject! With an exceptional team of professional academic experts in a wide range of subjects, we can guarantee you an unrivaled quality of custom-written papers.

Get ZERO PLAGIARISM, HUMAN WRITTEN ESSAYS

Why Hire Collepals.com writers to do your paper?

Quality- We are experienced and have access to ample research materials.

We write plagiarism Free Content

Confidential- We never share or sell your personal information to third parties.

Support-Chat with us today! We are always waiting to answer all your questions.