+1 (208) 254-6996 essayswallet@gmail.com
  

Week 3 Assignment 

University of Cumberland’s, Williamsburg, KY

Don't use plagiarized sources. Get Your Custom Essay on
Week 3 Assignment
Just from $13/Page
Order Essay

ITS 531-B05 – Business Intelligence 

Dr. Kelly Bruning

Complete the following assignment in one MS word document:

Chapter 5 –discussion question #1-4 & exercise 6 & internet exercise #7 (go to neuroshell.com click on the examples and look at the current examples. The Gee Whiz example is no longer on the page.)

Chapter 6– discussion question #1-5 & exercise 4

When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.

All work must be original (not copied from any source).

Textbook

Reference 

Sharda, R., Delen, D., & Turban, E. (2019). Analytics, Data Science, & Artificial Intelligence: Systems for Decision-Support 11th Edison. 

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

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

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 …

Order your essay today and save 10% with the discount code ESSAYHELP