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Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support 11th edition [Hardback]

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  • Formāts: Hardback, 832 pages, height x width x depth: 258x212x38 mm, weight: 1700 g
  • Izdošanas datums: 18-Feb-2019
  • Izdevniecība: Pearson
  • ISBN-10: 0135192013
  • ISBN-13: 9780135192016
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Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support 11th editionPalielināt
  • Formāts: Hardback, 832 pages, height x width x depth: 258x212x38 mm, weight: 1700 g
  • Izdošanas datums: 18-Feb-2019
  • Izdevniecība: Pearson
  • ISBN-10: 0135192013
  • ISBN-13: 9780135192016
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780135192016.html
Keywords:
"The purpose of this book is to introduce the reader to these technologies that are generally called analytics but have been known by other names. The core technology consists of DSS, BI, and various decision-making techniques. We use these terms interchangeably"--

For courses in decision support systems, computerized decision-making tools, and management support systems.
Market-leading guide to modern analytics, for better business decisions
 Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organizations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganization reflecting a new focus – analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.

Preface xxv
About the Authors xxxiv
Part I Introduction to Analytics and AI 1(192)
Chapter 1 Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support
2(71)
1.1 Opening Vignette: How Intelligent Systems Work for KONE Elevators and Escalators Company
3(2)
1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics
5(4)
Decision-Making Process
6(1)
The Influence of the External and Internal Environments on the Process
6(1)
Data and Its Analysis in Decision Making
7(1)
Technologies for Data Analysis and Decision Support
7(2)
1.3 Decision-Making Processes and Computerized Decision Support Framework
9(13)
Simon's Process: Intelligence, Design, and Choice
9(1)
The Intelligence Phase: Problem (or Opportunity) Identification
10(2)
Application Case 1.1 Making Elevators Go Faster!
11(1)
The Design Phase
12(1)
The Choice Phase
13(1)
The Implementation Phase
13(1)
The Classical Decision Support System Framework
14(4)
A DSS Application 16 Components of a Decision Support System
18(1)
The Data Management Subsystem
18(1)
The Model Management Subsystem
19(1)
Application Case 1.2 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions
20(1)
The User Interface Subsystem
20(1)
The Knowledge-Based Management Subsystem
21(1)
1.4 Evolution of Computerized Decision Support to Business Intelligence/Analytics/Data Science
22(8)
A Framework for Business Intelligence
25(1)
The Architecture of BI
25(1)
The Origins and Drivers of BI
26(1)
Data Warehouse as a Foundation for Business Intelligence
27(1)
Transaction Processing versus Analytic Processing
27(1)
A Multimedia Exercise in Business Intelligence
28(2)
1.5 Analytics Overview
30(8)
Descriptive Analytics
32(1)
Application Case 1.3 Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities
32(1)
Application Case 1.4 Siemens Reduces Cost with the Use of Data Visualization
33(1)
Predictive Analytics
33(1)
Application Case 1.5 Analyzing Athletic Injuries
34(1)
Prescriptive Analytics
34(4)
Application Case 1.6 A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates
35(3)
1.6 Analytics Examples in Selected Domains
38(14)
Sports Analytics-An Exciting Frontier for Learning and Understanding Applications of Analytics
38(5)
Analytics Applications in Healthcare-Humana Examples
43(9)
Application Case 1.7 Image Analysis Helps Estimate Plant Cover
50(2)
1.7 Artificial Intelligence Overview
52(7)
What Is Artificial Intelligence?
52(1)
The Major Benefits of AI
52(1)
The Landscape of AI
52(3)
Application Case 1.8 AI Increases Passengers' Comfort and Security in Airports and Borders
54(1)
The Three Flavors of AI Decisions
55(1)
Autonomous AI
55(1)
Societal Impacts
56(3)
Application Case 1.9 Robots Took the Job of Camel-Racing Jockeys for Societal Benefits
58(1)
1.8 Convergence of Analytics and AI
59(4)
Major Differences between Analytics and AI
59(1)
Why Combine Intelligent Systems?
60(1)
How Convergence Can Help?
60(1)
Big Data Is Empowering AI Technologies
60(1)
The Convergence of AI and the IoT
61(1)
The Convergence with Blockchain and Other Technologies
62(1)
Application Case 1.10 Amazon Go Is Open for Business
62(1)
IBM and Microsoft Support for Intelligent Systems Convergence
63(1)
1.9 Overview of the Analytics Ecosystem
63(2)
1.10 Plan of the Book
65(1)
1.11 Resources, Links, and the Teradata University Network Connection
66(1)
Resources and Links
66(1)
Vendors, Products, and Demos
66(1)
Periodicals
67(1)
The Teradata University Network Connection
67(1)
The Book's Web Site
67(1)
Chapter Highlights
67(1)
Key Terms
68(1)
Questions for Discussion
68(1)
Exercises
69(1)
References
70(3)
Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications
73(44)
2.1 Opening Vignette: INRIX Solves Transportation Problems
74(2)
2.2 Introduction to Artificial Intelligence
76(7)
Definitions
76(1)
Major Characteristics of AI Machines
77(1)
Major Elements of AI
77(1)
AI Applications
78(1)
Major Goals of AI
78(1)
Drivers of AI
79(1)
Benefits of AI
79(2)
Some Limitations of AI Machines
81(1)
Three Flavors of AI Decisions
81(1)
Artificial Brain
82(1)
2.3 Human and Computer Intelligence
83(4)
What Is Intelligence?
83(1)
How Intelligent Is AI?
84(1)
Measuring AI
85(2)
Application Case 2.1 How Smart Can a Vacuum Cleaner Be?
86(1)
2.4 Major AI Technologies and Some Derivatives
87(8)
Intelligent Agents
87(1)
Machine Learning
88(2)
Application Case 2.2 How Machine Learning Is Improving Work in Business
89(1)
Machine and Computer Vision
90(1)
Robotic Systems
91(1)
Natural Language Processing
92(1)
Knowledge and Expert Systems and Recommenders
93(1)
Chatbots
94(1)
Emerging AI Technologies
94(1)
2.5 AI Support for Decision Making
95(4)
Some Issues and Factors in Using AI in Decision Making
96(1)
AI Support of the Decision-Making Process
96(1)
Automated Decision Making
97(1)
Application Case 2.3 How Companies Solve Real-World Problems Using Google's Machine-Learning Tools
97(1)
Conclusion
98(1)
2.6 AI Applications in Accounting
99(2)
AI in Accounting: An Overview
99(1)
AI in Big Accounting Companies
100(1)
Accounting Applications in Small Firms
100(1)
Application Case 2.4 How EY, Deloitte, and PwC Are Using AI
100(1)
Job of Accountants
101(1)
2.7 AI Applications in Financial Services
101(4)
AI Activities in Financial Services
101(1)
AI in Banking: An Overview
101(1)
Illustrative AI Applications in Banking
102(1)
Insurance Services
103(2)
Application Case 2.5 US Bank Customer Recognition and Services
104(1)
2.8 AI in Human Resource Management (HRM)
105(2)
AI in HRM: An Overview
105(1)
AI in Onboarding
105(1)
Application Case 2.6 How Alexander Mann Solutions (AMS) Is Using AI to Support the Recruiting Process
106(1)
Introducing AI to HRM Operations
106(1)
2.9 AI in Marketing, Advertising, and CRM
107(3)
Overview of Major Applications
107(1)
AI Marketing Assistants in Action
108(1)
Customer Experiences and CRM
108(2)
Application Case 2.7 Kraft Foods Uses AI for Marketing and CRM
109(1)
Other Uses of AI in Marketing
110(1)
2.10 AI Applications in Production-Operation Management (POM)
110(2)
AI in Manufacturing
110(1)
Implementation Model
111(1)
Intelligent Factories
111(1)
Logistics and Transportation
112(1)
Chapter Highlights
112(1)
Key Terms
113(1)
Questions for Discussion
113(1)
Exercises
114(1)
References
114(3)
Chapter 3 Nature of Data, Statistical Modeling, and Visualization
117(76)
3.1 Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing
118(3)
3.2 Nature of Data
121(4)
3.3 Simple Taxonomy of Data
125(4)
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(2)
3.4 Art and Science of Data Preprocessing
129(10)
Application Case 3.2 Improving Student Retention with Data-Driven Analytics
133(6)
3.5 Statistical Modeling for Business Analytics
139(12)
Descriptive Statistics for Descriptive Analytics
140(1)
Measures of Centrality Tendency (Also Called Measures of Location or Centrality)
140(1)
Arithmetic Mean
140(1)
Median
141(1)
Mode
141(1)
Measures of Dispersion (Also Called Measures of Spread or Decentrality)
142(1)
Range
142(1)
Variance
142(1)
Standard Deviation
143(1)
Mean Absolute Deviation
143(1)
Quartiles and Interquartile Range
143(1)
Box-and-Whiskers Plot
143(2)
Shape of a Distribution
145(6)
Application Case 3.3 Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and Detect Problems
150(1)
3.6 Regression Modeling for Inferential Statistics
151(12)
How Do We Develop the Linear Regression Model?
152(1)
How Do We Know If the Model Is Good Enough?
153(1)
What Are the Most Important Assumptions in Linear Regression?
154(1)
Logistic Regression
155(1)
Time-Series Forecasting
156(10)
Application Case 3.4 Predicting NCAA Bowl Game Outcomes
157(6)
3.7 Business Reporting
163(3)
Application Case 3.5 Flood of Paper Ends at FEMA
165(1)
3.8 Data Visualization
166(5)
Brief History of Data Visualization
167(4)
Application Case 3.6 Macfarlan Smith Improves Operational Performance Insight with Tableau Online
169(2)
3.9 Different Types of Charts and Graphs
171(5)
Basic Charts and Graphs
171(1)
Specialized Charts and Graphs
172(2)
Which Chart or Graph Should You Use?
174(2)
3.10 Emergence of Visual Analytics
176(6)
Visual Analytics
178(2)
High-Powered Visual Analytics Environments
180(2)
3.11 Information Dashboards
182(6)
Application Case 3.7 Dallas Cowboys Score Big with Tableau and Teknion
184(1)
Dashboard Design
184(2)
Application Case 3.8 Visual Analytics Helps Energy Supplier Make Better Connections
185(1)
What to Look for in a Dashboard
186(1)
Best Practices in Dashboard Design
187(1)
Benchmark Key Performance Indicators with Industry Standards
187(1)
Wrap the Dashboard Metrics with Contextual Metadata
187(1)
Validate the Dashboard Design by a Usability Specialist
187(1)
Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard
188(1)
Enrich the Dashboard with Business-User Comments
188(1)
Present Information in Three Different Levels
188(1)
Pick the Right Visual Construct Using Dashboard Design Principles
188(1)
Provide for Guided Analytics
188(1)
Chapter Highlights
188(1)
Key Terms
189(1)
Questions for Discussion
190(1)
Exercises
190(2)
References
192(1)
Part II Predictive Analytics/Machine Learning 193(266)
Chapter 4 Data Mining Process, Methods, and Algorithms
194(57)
4.1 Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime
195(3)
4.2 Data Mining Concepts
198(10)
Application Case 4.1 Visa Is Enhancing the Customer Experience while Reducing Fraud with Predictive Analytics and Data Mining
199(2)
Definitions, Characteristics, and Benefits
201(1)
How Data Mining Works
202(6)
Application Case 4.2 American Honda Uses Advanced Analytics to Improve Warranty Claims
203(5)
Data Mining Versus Statistics
208(1)
4.3 Data Mining Applications
208(3)
Application Case 4.3 Predictive Analytic and Data Mining Help Stop Terrorist Funding
210(1)
4.4 Data Mining Process
211(9)
Step 1: Business Understanding
212(1)
Step 2: Data Understanding
212(1)
Step 3: Data Preparation
213(1)
Step 4: Model Building
214(3)
Application Case 4.4 Data Mining Helps in Cancer Research
214(3)
Step 5: Testing and Evaluation
217(1)
Step 6: Deployment
217(1)
Other Data Mining Standardized Processes and Methodologies
217(3)
4.5 Data Mining Methods
220(16)
Classification
220(1)
Estimating the True Accuracy of Classification Models
221(3)
Estimating the Relative Importance of Predictor Variables
224(4)
Cluster Analysis for Data Mining
228(4)
Application Case 4.5 Influence Health Uses Advanced Predictive Analytics to Focus on the Factors That Really Influence People's Healthcare Decisions
229(3)
Association Rule Mining
232(4)
4.6 Data Mining Software Tools
236(6)
Application Case 4.6 Data Mining goes to Hollywood: Predicting Financial Success of Movies
239(3)
4.7 Data Mining Privacy Issues, Myths, and Blunders
242(4)
Application Case 4.7 Predicting Customer Buying Patterns-The Target Story
243(1)
Data Mining Myths and Blunders
244(2)
Chapter Highlights
246(1)
Key Terms
247(1)
Questions for Discussion
247(1)
Exercises
248(2)
References
250(1)
Chapter 5 Machine-Learning Techniques for Predictive Analytics
251(64)
5.1 Opening Vignette: Predictive Modeling Helps Better Understand and Manage Complex Medical Procedures
252(3)
5.2 Basic Concepts of Neural Networks
255(4)
Biological versus Artificial Neural Networks
256(3)
Application Case 5.1 Neural Networks are Helping to Save Lives in the Mining Industry
258(1)
5.3 Neural Network Architectures
259(4)
Kohonen's Self-Organizing Feature Maps
259(1)
Hopfield Networks
260(3)
Application Case 5.2 Predictive Modeling Is Powering the Power Generators
261(2)
5.4 Support Vector Machines
263(8)
Application Case 5.3 Identifying Injury Severity Risk Factors in Vehicle Crashes with Predictive Analytics
264(5)
Mathematical Formulation of SVM
269(1)
Primal Form
269(1)
Dual Form
269(1)
Soft Margin
270(1)
Nonlinear Classification
270(1)
Kernel Trick
271(1)
5.5 Process-Based Approach to the Use of SVM
271(3)
Support Vector Machines versus Artificial Neural Networks
273(1)
5.6 Nearest Neighbor Method for Prediction
274(4)
Similarity Measure: The Distance Metric
275(1)
Parameter Selection
275(3)
Application Case 5.4 Efficient Image Recognition and Categorization with knn
277(1)
5.7 Naive Bayes Method for Classification
278(9)
Bayes Theorem
279(1)
Naive Bayes Classifier
279(1)
Process of Developing a Naive Bayes Classifier
280(1)
Testing Phase
281(6)
Application Case 5.5 Predicting Disease Progress in Crohn's Disease Patients: A Comparison of Analytics Methods
282(5)
5.8 Bayesian Networks
287(6)
How Does BN Work?
287(1)
How Can BN Be Constructed?
288(5)
5.9 Ensemble Modeling
293(13)
Motivation-Why Do We Need to Use Ensembles?
293(2)
Different Types of Ensembles
295(1)
Bagging
296(2)
Boosting
298(1)
Variants of Bagging and Boosting
299(1)
Stacking
300(1)
Information Fusion
300(1)
Summary-Ensembles are not Perfect!
301(33)
Application Case 5.6 To Imprison or Not to Imprison: A Predictive Analytics-Based Decision Support System for Drug Courts
304(2)
Chapter Highlights
306(2)
Key Terms
308(1)
Questions for Discussion
308(1)
Exercises
309(3)
Internet Exercises
312(1)
References
313(2)
Chapter 6 Deep Learning and Cognitive Computing
315(73)
6.1 Opening Vignette: Fighting Fraud with Deep Learning and Artificial Intelligence
316(4)
6.2 Introduction to Deep Learning
320(5)
Application Case 6.1 Finding the Next Football Star with Artificial Intelligence
323(2)
6.3 Basics of "Shallow" Neural Networks
325(9)
Application Case 6.2 Gaming Companies Use Data Analytics to Score Points with Players
328(5)
Application Case 6.3 Artificial Intelligence Helps Protect Animals from Extinction
333(1)
6.4 Process of Developing Neural Network-Based Systems
334(6)
Learning Process in ANN
335(1)
Backpropagation for ANN Training
336(4)
6.5 Illuminating the Black Box of ANN
340(3)
Application Case 6.4 Sensitivity Analysis Reveals Injury Severity Factors in Traffic Accidents
341(2)
6.6 Deep Neural Networks
343(6)
Feedforward Multilayer Perceptron (MLP)-Type Deep Networks
343(1)
Impact of Random Weights in Deep MLP
344(1)
More Hidden Layers versus More Neurons?
345(4)
Application Case 6.5 Georgia DOT Variable Speed Limit Analytics Help Solve Traffic Congestions
346(3)
6.7 Convolutional Neural Networks
349(11)
Convolution Function
349(3)
Pooling
352(1)
Image Processing Using Convolutional Networks
353(4)
Application Case 6.6 From Image Recognition to Face Recognition
356(1)
Text Processing Using Convolutional Networks
357(3)
6.8 Recurrent Networks and Long Short-Term Memory Networks
360(8)
Application Case 6.7 Deliver Innovation by Understanding Customer Sentiments
363(2)
LSTM Networks Applications
365(3)
6.9 Computer Frameworks for Implementation of Deep Learning
368(2)
Torch
368(1)
Caffe
368(1)
TensorFlow
369(1)
Theano
369(1)
Keras: An Application Programming Interface
370(1)
6.10 Cognitive Computing
370(11)
How Does Cognitive Computing Work?
371(1)
How Does Cognitive Computing Differ from AI?
372(2)
Cognitive Search
374(1)
IBM Watson: Analytics at Its Best
375(2)
Application Case 6.8 IBM Watson Competes against the Best at Jeopardy!
376(1)
How Does Watson Do It?
377(1)
What Is the Future for Watson?
377(4)
Chapter Highlights
381(2)
Key Terms
383(1)
Questions for Discussion
383(1)
Exercises
384(1)
References
385(3)
Chapter 7 Text Mining, Sentiment Analysis, and Social Analytics
388(71)
7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into Near-Real-Time Sales
389(3)
7.2 Text Analytics and Text Mining Overview
392(5)
Application Case 7.1 Netflix: Using Big Data to Drive Big Engagement: Unlocking the Power of Analytics to Drive Content and Consumer Insight
395(2)
7.3 Natural Language Processing (NLP)
397(5)
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(3)
7.4 A Text Mining Applications
402(8)
Marketing Applications
403(1)
Security Applications
403(1)
Biomedical Applications
404(3)
Application Case 7.3 Mining for Lies
404(3)
Academic Applications
407(3)
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(2)
7.5 Text Mining Process
410(8)
Task 1: Establish the Corpus
410(1)
Task 2: Create the Term-Document Matrix
411(2)
Task 3: Extract the Knowledge
413(5)
Application Case 7.5 Research Literature Survey with Text Mining
415(3)
7.6 Sentiment Analysis
418(11)
Application Case 7.6 Creating a Unique Digital Experience to Capture Moments That Matter at Wimbledon
419(3)
Sentiment Analysis Applications
422(2)
Sentiment Analysis Process
424(2)
Methods for Polarity Identification
426(1)
Using a Lexicon
426(1)
Using a Collection of Training Documents
427(1)
Identifying Semantic Orientation of Sentences and Phrases
428(1)
Identifying Semantic Orientation of Documents
428(1)
7.7 Web Mining Overview
429(4)
Web Content and Web Structure Mining
431(2)
7.8 Search Engines
433(8)
Anatomy of a Search Engine
434(2)
1 Development Cycle
434(1)
2 Response Cycle
435(1)
Search Engine Optimization
436(1)
Methods for Search Engine Optimization
437(4)
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(2)
7.9 Web Usage Mining (Web Analytics)
441(5)
Web Analytics Technologies
441(1)
Web Analytics Metrics
442(1)
Web Site Usability
442(1)
Traffic Sources
443(1)
Visitor Profiles
444(1)
Conversion Statistics
444(2)
7.10 Social Analytics
446(9)
Social Network Analysis
446(1)
Social Network Analysis Metrics
447(3)
Application Case 7.8 Tito's Vodka Establishes Brand Loyalty with an Authentic Social Strategy
447(3)
Connections
450(1)
Distributions
450(1)
Segmentation
451(1)
Social Media Analytics
451(1)
How Do People Use Social Media?
452(1)
Measuring the Social Media Impact
453(1)
Best Practices in Social Media Analytics
453(2)
Chapter Highlights
455(1)
Key Terms
456(1)
Questions for Discussion
456(1)
Exercises
456(1)
References
457(2)
Part III Prescriptive Analytics and Big Data 459(120)
Chapter 8 Prescriptive Analytics: Optimization and Simulation
460(49)
8.1 Opening Vignette: School District of Philadelphia Uses Prescriptive Analytics to Find Optimal Solution for Awarding Bus Route Contracts
461(1)
8.2 Model-Based Decision Making
462(7)
Application Case 8.1 Canadian Football League Optimizes Game Schedule
463(2)
Prescriptive Analytics Model Examples
465(1)
Identification of the Problem and Environmental Analysis
465(2)
Application Case 8.2 Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions
466(1)
Model Categories
467(2)
8.3 Structure of Mathematical Models for Decision Support
469(2)
The Components of Decision Support Mathematical Models
469(1)
The Structure of Mathematical Models
470(1)
8.4 Certainty, Uncertainty, and Risk
471(2)
Decision Making under Certainty
471(1)
Decision Making under Uncertainty
472(1)
Decision Making under Risk (Risk Analysis)
472(5)
Application Case 8.3 American Airlines Uses Should-Cost Modeling to Assess the Uncertainty of Bids for Shipment Routes
472(1)
8.5 Decision Modeling with Spreadsheets
473(4)
Application Case 8.4 Pennsylvania Adoption Exchange Uses Spreadsheet Model to Better Match Children with Families
474(1)
Application Case 8.5 Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Routes
475(2)
8.6 Mathematical Programming Optimization
477(9)
Application Case 8.6 Mixed-Integer Programming Model Helps the University of Tennessee Medical Center with Scheduling Physicians
478(1)
Linear Programming Model
479(1)
Modeling in LP: An Example
480(4)
Implementation
484(2)
8.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
486(4)
Multiple Goals
486(1)
Sensitivity Analysis
487(1)
What-If Analysis
488(1)
Goal Seeking
489(1)
8.8 Decision Analysis with Decision Tables and Decision Trees
490(3)
Decision Tables
490(2)
Decision Trees
492(1)
8.9 Introduction to Simulation
493(7)
Major Characteristics of Simulation
493(1)
Application Case 8.7 Steel Tubing Manufacturer Uses a Simulation-Based Production Scheduling System
493(1)
Advantages of Simulation
494(1)
Disadvantages of Simulation
495(1)
The Methodology of Simulation
495(1)
Simulation Types
496(1)
Monte Carlo Simulation
497(1)
Discrete Event Simulation
498(2)
Application Case 8.8 Cosan Improves Its Renewable Energy Supply Chain Using Simulation
498(2)
8.10 Visual Interactive Simulation
500(5)
Conventional Simulation Inadequacies
500(1)
Visual Interactive Simulation
500(1)
Visual Interactive Models and DSS
500(1)
Simulation Software
501(12)
Application Case 8.9 Improving Job-Shop Scheduling Decisions through RFID: A Simulation-Based Assessment
501(4)
Chapter Highlights
505(1)
Key Terms
505(1)
Questions for Discussion
505(1)
Exercises
506(2)
References
508(1)
Chapter 9 Big Data, Cloud Computing, and Location Analytics: Concepts and Tools
509(70)
9.1 Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data Methods
510(3)
9.2 Definition of Big Data
513(6)
The "V"s That Define Big Data
514(5)
Application Case 9.1 Alternative Data for Market Analysis or Forecasts
517(2)
9.3 Fundamentals of Big Data Analytics
519(4)
Business Problems Addressed by Big Data Analytics
521(2)
Application Case 9.2 Overstock.com Combines Multiple Datasets to Understand Customer Journeys
522(1)
9.4 Big Data Technologies
523(9)
MapReduce
523(1)
Why Use MapReduce?
523(1)
Hadoop
524(1)
How Does Hadoop Work?
525(1)
Hadoop Technical Components
525(2)
Hadoop: The Pros and Cons
527(1)
NoSQL
528(4)
Application Case 9.3 eBay's Big Data Solution
529(2)
Application Case 9.4 Understanding Quality and Reliability of Healthcare Support Information on Twitter
531(1)
9.5 Big Data and Data Warehousing
532(5)
Use Cases for Hadoop
533(1)
Use Cases for Data Warehousing
534(1)
The Gray Areas (Any One of the Two Would Do the Job)
535(1)
Coexistence of Hadoop and Data Warehouse
536(1)
9.6 In-Memory Analytics and Apache Spark™
537(6)
Application Case 9.5 Using Natural Language Processing to analyze customer feedback in TripAdvisor reviews
538(1)
Architecture of Apache Spark™
538(1)
Getting Started with Apache Spark™
539(4)
9.7 Big Data and Stream Analytics
543(6)
Stream Analytics versus Perpetual Analytics
544(1)
Critical Event Processing
545(1)
Data Stream Mining
546(1)
Applications of Stream Analytics
546(1)
e-Commerce
546(1)
Telecommunications
546(1)
Application Case 9.6 Salesforce Is Using Streaming Data to Enhance Customer Value
547(1)
Law Enforcement and Cybersecurity
547(1)
Power Industry
548(1)
Financial Services
548(1)
Health Sciences
548(1)
Government
548(1)
9.8 Big Data Vendors and Platforms
549(8)
Infrastructure Services Providers
550(1)
Analytics Solution Providers
550(1)
Business Intelligence Providers Incorporating Big Data
551(6)
Application Case 9.7 Using Social Media for Nowcasting Flu Activity
551(3)
Application Case 9.8 Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse
554(3)
9.9 Cloud Computing and Business Analytics
557(10)
Data as a Service (DaaS)
558(1)
Software as a Service (SaaS)
559(1)
Platform as a Service (PaaS)
559(1)
Infrastructure as a Service (IaaS)
559(1)
Essential Technologies for Cloud Computing
560(3)
Application Case 9.9 Major West Coast Utility Uses Cloud-Mobile Technology to Provide Real-Time Incident Reporting
561(2)
Cloud Deployment Models
563(1)
Major Cloud Platform Providers in Analytics
563(1)
Analytics as a Service (AaaS)
564(1)
Representative Analytics as a Service Offerings
564(1)
Illustrative Analytics Applications Employing the Cloud Infrastructure
565(1)
Using Azure 10T, Stream Analytics, and Machine Learning to Improve Mobile Health Care Services
565(1)
Gulf Air Uses Big Data to Get Deeper Customer Insight
566(1)
Chime Enhances Customer Experience Using Snowflake
566(1)
9.10 Location-Based Analytics for Organizations
567(7)
Geospatial Analytics
567(5)
Application Case 9.10 Great Clips Employs Spatial Analytics to Shave Time in Location Decisions
570(1)
Application Case 9.11 Starbucks Exploits GIS and Analytics to Grow Worldwide
570(2)
Real-Time Location Intelligence
572(1)
Analytics Applications for Consumers
573(1)
Chapter Highlights
574(1)
Key Terms
575(1)
Questions for Discussion
575(1)
Exercises
575(1)
References
576(3)
Part IV Robotics, Social Networks, AI and IoT 579(146)
Chapter 10 Robotics: Industrial and Consumer Applications
580(30)
10.1 Opening Vignette: Robots Provide Emotional Support to Patients and Children
581(3)
10.2 Overview of Robotics
584(1)
10.3 History of Robotics
584(2)
10.4 Illustrative Applications of Robotics
586(9)
Changing Precision Technology
586(1)
Adidas
586(1)
BMW Employs Collaborative Robots
587(1)
Tega
587(1)
San Francisco Burger Eatery
588(1)
Spyce
588(1)
Mahindra & Mahindra Ltd.
589(1)
Robots in the Defense Industry
589(1)
Pepper
590(2)
Da Vinci Surgical System
592(1)
Snoo - A Robotic Crib
593(1)
MEDi
593(1)
Care-E Robot
593(1)
AGROBOT
594(1)
10.5 Components of Robots
595(1)
10.6 Various Categories of Robots
596(1)
10.7 Autonomous Cars: Robots in Motion
597(3)
Autonomous Vehicle Development
598(1)
Issues with Self-Driving Cars
599(1)
10.8 Impact of Robots on Current and Future Jobs
600(3)
10.9 Legal Implications of Robots and Artificial Intelligence
603(3)
Tort Liability
603(1)
Patents
603(1)
Property
604(1)
Taxation
604(1)
Practice of Law
604(1)
Constitutional Law
605(1)
Professional Certification
605(1)
Law Enforcement
605(1)
Chapter Highlights
606(1)
Key Terms
606(1)
Questions for Discussion
606(1)
Exercises
607(1)
References
607(3)
Chapter 11 Group Decision Making, Collaborative Systems, and AI Support
610(38)
11.1 Opening Vignette: Hendrick Motorsports Excels with Collaborative Teams
611(2)
11.2 Making Decisions in Groups: Characteristics, Process, Benefits, and Dysfunctions
613(3)
Characteristics of Group Work
613(1)
Types of Decisions Made by Groups
614(1)
Group Decision-Making Process
614(1)
Benefits and Limitations of Group Work
615(1)
11.3 Supporting Group Work and Team Collaboration with Computerized Systems
616(3)
Overview of Group Support Systems (GSS)
617(1)
Time/Place Framework
617(1)
Group Collaboration for Decision Support
618(1)
11.4 Electronic Support for Group Communication and Collaboration
619(4)
Groupware for Group Collaboration
619(1)
Synchronous versus Asynchronous Products
619(1)
Virtual Meeting Systems
620(2)
Collaborative Networks and Hubs
622(1)
Collaborative Hubs
622(1)
Social Collaboration
622(1)
Sample of Popular Collaboration Software
623(1)
11.5 Direct Computerized Support for Group Decision Making
623(6)
Group Decision Support Systems (GDSS)
624(1)
Characteristics of GDSS
625(1)
Supporting the Entire Decision-Making Process
625(2)
Brainstorming for Idea Generation and Problem Solving
627(1)
Group Support Systems
628(1)
11.6 Collective Intelligence and Collaborative Intelligence
629(4)
Definitions and Benefits
629(1)
Computerized Support to Collective Intelligence
629(2)
Application Case 11.1 Collaborative Modeling for Optimal Water Management: The Oregon State University Project
630(1)
How Collective Intelligence May Change Work and Life
631(1)
Collaborative Intelligence
632(1)
How to Create Business Value from Collaboration: The IBM Study
632(1)
11.7 Crowdsourcing as a Method for Decision Support
633(3)
The Essentials of Crowdsourcing
633(1)
Crowdsourcing for Problem-Solving and Decision Support
634(1)
Implementing Crowdsourcing for Problem Solving
635(1)
Application Case 11.2 How InnoCentive Helped GSK Solve a Difficult Problem
636(1)
11.8 Artificial Intelligence and Swarm AI Support of Team Collaboration and Group Decision Making
636(4)
AI Support of Group Decision Making
637(1)
AI Support of Team Collaboration
637(2)
Swarm Intelligence and Swarm AI
639(1)
Application Case 11.3 XPRIZE Optimizes Visioneering
639(1)
11.9 Human-Machine Collaboration and Teams of Robots
640(4)
Human-Machine Collaboration in Cognitive Jobs
641(1)
Robots as Coworkers: Opportunities and Challenges
641(1)
Teams of collaborating Robots
642(2)
Chapter Highlights
644(1)
Key Terms
645(1)
Questions for Discussion
645(1)
Exercises
645(1)
References
646(2)
Chapter 12 Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, and Robo Advisors
648(39)
12.1 Opening Vignette: Sephora Excels with Chatbots
649(1)
12.2 Expert Systems and Recommenders
650(10)
Basic Concepts of Expert Systems (ES)
650(2)
Characteristics and Benefits of ES
652(1)
Typical Areas for ES Applications
653(1)
Structure and Process of ES
653(2)
Application Case 12.1 ES Aid in Identification of Chemical, Biological, and Radiological Agents
655(1)
Why the Classical Type of ES Is Disappearing
655(2)
Application Case 12.2 VisiRule
656(1)
Recommendation Systems
657(3)
Application Case 12.3 Netflix Recommender: A Critical Success Factor
658(2)
12.3 Concepts, Drivers, and Benefits of Chatbots
660(4)
What Is a Chatbot?
660(1)
Chatbot Evolution
660(2)
Components of Chatbots and the Process of Their Use
662(1)
Drivers and Benefits
663(1)
Representative Chatbots from Around the World
663(1)
12.4 Enterprise Chatbots
664(8)
The Interest of Enterprises in Chatbots
664(1)
Enterprise Chatbots: Marketing and Customer Experience
665(3)
Application Case 12.4 WeChat's Super Chatbot
666(1)
Application Case 12.5 How Vera Gold Mark Uses Chatbots to Increase Sales
667(1)
Enterprise Chatbots: Financial Services
668(1)
Enterprise Chatbots: Service Industries
668(1)
Chatbot Platforms
669(2)
Application Case 12.6 Transavia Airlines Uses Bots for Communication and Customer Care Delivery
669(2)
Knowledge for Enterprise Chatbots
671(1)
12.5 Virtual Personal Assistants
672(4)
Assistant for Information Search
672(1)
If You Were Mark Zuckerberg, Facebook CEO
672(1)
Amazon's Alexa and Echo
672(3)
Apple's Siri
675(1)
Google Assistant
675(1)
Other Personal Assistants
675(1)
Competition Among Large Tech Companies
675(1)
Knowledge for Virtual Personal Assistants
675(1)
12.6 Chatbots as Professional Advisors (Robo Advisors)
676(4)
Robo Financial Advisors
676(1)
Evolution of Financial Robo Advisors
676(1)
Robo Advisors 2.0: Adding the Human Touch
676(2)
Application Case 12.7 Betterment, the Pioneer of Financial Robo Advisors
677(1)
Managing Mutual Funds Using AI
678(1)
Other Professional Advisors
678(2)
IBM Watson
680(1)
12.7 Implementation Issues
680(3)
Technology Issues
680(1)
Disadvantages and Limitations of Bots
681(1)
Quality of Chatbots
681(1)
Setting Up Alexa's Smart Home System
682(1)
Constructing Bots
682(1)
Chapter Highlights
683(1)
Key Terms
683(1)
Questions for Discussion
684(1)
Exercises
684(1)
References
685(2)
Chapter 13 The Internet of Things as a Platform for Intelligent Applications
687(38)
13.1 Opening Vignette: CNH Industrial Uses the Internet of Things to Excel
688(1)
13.2 Essentials of IoT
689(5)
Definitions and Characteristics
690(1)
The IoT Ecosystem
691(1)
Structure of IoT Systems
691(3)
13.3 Major Benefits and Drivers of IoT
694(2)
Major Benefits of IoT
694(1)
Major Drivers of IoT
695(1)
Opportunities
695(1)
13.4 How IoT Works
696(1)
IoT and Decision Support
696(1)
13.5 Sensors and Their Role in IoT
697(4)
Brief Introduction to Sensor Technology
697(1)
Application Case 13.1 Using Sensors, IoT, and AI for Environmental Control at the Athens, Greece, International Airport
697(1)
How Sensors Work with IoT
698(1)
Application Case 13.2 Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets to Predict Failures
698(1)
Sensor Applications and Radio-Frequency Identification (RFID) Sensors
699(2)
13.6 Selected IoT Applications
701(2)
A Large-scale IoT in Action
701(1)
Examples of Other Existing Applications
701(2)
13.7 Smart Homes and Appliances
703(4)
Typical Components of Smart Homes
703(1)
Smart Appliances
704(2)
A Smart Home Is Where the Bot Is
706(1)
Barriers to Smart Home Adoption
707(1)
13.8 Smart Cities and Factories
707(7)
Application Case 13.3 Amsterdam on the Road to Become a Smart City
708(1)
Smart Buildings: From Automated to Cognitive Buildings
709(1)
Smart Components in Smart Cities and Smart Factories
709(3)
Application Case 13.4 How IBM Is Making Cities Smarter Worldwide
711(1)
Improving Transportation in the Smart City
712(1)
Combining Analytics and IoT in Smart City Initiatives
713(1)
Bill Gates' Futuristic Smart City
713(1)
Technology Support for Smart Cities
713(1)
13.9 Autonomous (Self-Driving) Vehicles
714(3)
The Developments of Smart Vehicles
714(3)
Application Case 13.5 Waymo and Autonomous Vehicles
715(2)
Flying Cars
717(1)
Implementation Issues in Autonomous Vehicles
717(1)
13.10 Implementing IoT and Managerial Considerations
717(4)
Major Implementation Issues
718(1)
Strategy for Turning Industrial IoT into Competitive Advantage
719(1)
The Future of the IoT
720(1)
Chapter Highlights
721(1)
Key Terms
721(1)
Questions for Discussion
722(1)
Exercises
722(1)
References
722(3)
Part V Caveats of Analytics and AI 725(45)
Chapter 14 Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts
726(44)
14.1 Opening Vignette: Why Did Uber Pay $245 Million to Waymo?
727(2)
14.2 Implementing Intelligent Systems: An Overview
729(2)
The Intelligent Systems Implementation Process
729(1)
The Impacts of Intelligent Systems
730(1)
14.3 Legal, Privacy, and Ethical Issues
731(6)
Legal Issues
731(1)
Privacy Issues
732(3)
Who Owns Our Private Data?
735(1)
Ethics Issues
735(1)
Ethical Issues of Intelligent Systems
736(1)
Other Topics in Intelligent Systems Ethics
736(1)
14.4 Successful Deployment of Intelligent Systems
737(3)
Top Management and Implementation
738(1)
System Development Implementation Issues
738(1)
Connectivity and Integration
739(1)
Security Protection
739(1)
Leveraging Intelligent Systems in Business
739(1)
Intelligent System Adoption
740(1)
14.5 Impacts of Intelligent Systems on Organizations
740(7)
New Organizational Units and Their Management
741(1)
Transforming Businesses and Increasing Competitive Advantage
741(2)
Application Case 14.1 How 1-800-Flowers.com Uses Intelligent Systems for Competitive Advantage
742(1)
Redesign of an Organization Through the Use of Analytics
743(1)
Intelligent Systems' Impact on Managers' Activities, Performance, and Job Satisfaction
744(1)
Impact on Decision Making
745(1)
Industrial Restructuring
746(1)
14.6 Impacts on Jobs and Work
747(6)
An Overview
747(1)
Are Intelligent Systems Going to Take Jobs-My Job?
747(1)
AI Puts Many Jobs at Risk
748(1)
Application Case 14.2 White-Collar Jobs That Robots Have Already Taken
748(1)
Which Jobs Are Most in Danger? Which Ones Are Safe?
749(1)
Intelligent Systems May Actually Add Jobs
750(1)
Jobs and the Nature of Work Will Change
751(1)
Conclusion: Let's Be Optimistic!
752(1)
14.7 Potential Dangers of Robots, AI, and Analytical Modeling
753(3)
Position of AI Dystopia
753(1)
The AI Utopia's Position
753(1)
The Open AI Project and the Friendly AI
754(1)
The O'Neil Claim of Potential Analytics' Dangers
755(1)
14.8 Relevant Technology Trends
756(4)
Gartner's Top Strategic Technology Trends for 2018 and 2019
756(1)
Other Predictions Regarding Technology Trends
757(1)
Summary: Impact on AI and Analytics
758(1)
Ambient Computing (Intelligence)
758(2)
14.9 Future of Intelligent Systems
760(4)
What Are the Major U.S. High-Tech Companies Doing in the Intelligent Technologies Field?
760(1)
AI Research Activities in China
761(3)
Application Case 14.3 How Alibaba.com Is Conducting AI
762(2)
The U.S.-China Competition: Who Will Control AI?
764(1)
The Largest Opportunity in Business
764(1)
Conclusion
764(1)
Chapter Highlights
765(1)
Key Terms
766(1)
Questions for Discussion
766(1)
Exercises
766(1)
References
767(3)
Glossary 770(15)
Index 785
About our authors Ramesh Sharda (MBA, PhD, University of WisconsinMadison) is Vice Dean for Research and Graduate Programs, Watson/ConocoPhillips Chair, and Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University. His research has been published in major journals in management science and information systems, includingManagement Science,Operations Research,Information Systems Research,Decision Support Systems,Decision Sciences Journal,EJIS,JMIS,Interfaces,INFORMS Journal on Computing, andACM Database. Dr. Sharda is a member of the editorial boards of journals such asDecision Support Systems,Decision Sciences, andACM Database. He has worked on many sponsored research projects with government and industry, and has been a consultant to many organizations. He also serves as the faculty director of Teradata University Network. Dr. Sharda received the 2013 INFORMS Computing Society HG Lifetime Service Award, and was inducted into the Oklahoma Higher Education Hall of Fame in 2016. He is a fellow of INFORMS.

Dursun Delen (PhD, Oklahoma State University) is the Spears and Patterson Chair in Business Analytics, Director of Research for the Center for Health Systems Innovation, and Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University. Prior to his academic career, he worked for a privately owned research and consultancy company, Knowledge Based Systems, Inc. in College Station, Texas, as a research scientist for five years, during which time he led a number of decision support and other information systemsrelated research projects funded by federal agencies such as DoD, NASA, NIST, and DOE. Dr. Delen's research has appeared in major journals, includingDecision Sciences,Decision Support Systems,Communications of the ACM,Computers and Operations Research,Computers in Industry,Journal of Production Operations Management,Journal of American Medical Informatics Association,Artificial Intelligence in Medicine, andExpert Systems with Applications. He has published eight books and textbooks and more than 100 peer-reviewed journal articles, and is often invited to deliver keynote addresses at national and international conferences on topics related to business analytics, Big Data, data/text mining, business intelligence, decision support systems, and knowledge management. Dr. Delen served as the general co-chair for the 4th International Conference on Network Computing and Advanced Information Management in Seoul, South Korea, and regularly serves as chair on tracks and mini-tracks at various business analytics and information systems conferences. He is the co-editor-in-chief of theJournal of Business Analytics, the area editor forBig Data and Business Analytics on the Journal of Business Research, and chief editor, senior editor, associate editor, and editorial board member on more than a dozen other journals. His consultancy, research, and teaching interests are in business analytics, data and text mining, health analytics, decision support systems, knowledge management, systems analysis and design, and enterprise modeling.

Efraim Turban (MBA, PhD, University of California, Berkeley) is a visiting scholar at the Pacific Institute for Information System Management, University of Hawaii. Prior to this, he was on the staff of several universities, including City University of Hong Kong; Lehigh University; Florida International University; California State University, Long Beach; Eastern Illinois University; and the University of Southern California. Dr. Turban is the author of more than 110 refereed papers published in leading journals such asManagement Science,MIS Quarterly, andDecision Support Systems. He is also the author of 22 books, includingElectronic Commerce: A Managerial PerspectiveandInformation Technology for Management. Dr. Turban is a consultant to major corporations worldwide. His current areas of interest are web-based decision support systems, digital commerce, and applied artificial intelligence.