Systems for Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support, Global Edition
Lýsing:For courses in decision support systems, computerized decision-making tools, and...
4.690 kr.
Lýsing:
For courses in decision support systems, computerized decision-making tools, and management support systems. 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.
Annað
- Höfundar: Ramesh Sharda, Dursun Delen, Efraim Turban
- Útgáfa:11
- Útgáfudagur: 2020-03-10
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- Format:Page Fidelity
- ISBN 13: 9781292341606
- Print ISBN: 9781292341552
- ISBN 10: 1292341602
Efnisyfirlit
- Title Page
- Copyright Page
- Brief Contents
- Contents
- Preface
- About the Authors
- Part I Introduction to Analytics and AI
- Chapter 1 Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence:
- 1.1 Opening Vignette: How Intelligent Systems Work for KONE Elevators and Escalators Company
- 1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics
- Decision-Making Process
- The Influence of the External and Internal Environments on the Process
- Data and Its Analysis in Decision Making
- Technologies for Data Analysis and Decision Support
- 1.3 Decision-Making Processes and Computerized Decision Support Framework
- Simon’s Process: Intelligence, Design, and Choice
- The Intelligence Phase: Problem (or Opportunity) Identification
- Application Case 1.1 Making Elevators Go Faster
- The Design Phase
- The Choice Phase
- The Implementation Phase
- The Classical Decision Support System Framework
- A DSS Application
- Components of a Decision Support System
- The Data Management Subsystem
- The Model Management Subsystem
- Application Case 1.2 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions
- The User Interface Subsystem
- The Knowledge-Based Management Subsystem
- 1.4 Evolution of Computerized Decision Support to Business Intelligence/Analytics/Data Science
- A Framework for Business Intelligence
- The Architecture of BI
- The Origins and Drivers of BI
- Data Warehouse as a Foundation for Business Intelligence
- Transaction Processing versus Analytic Processing
- A Multimedia Exercise in Business Intelligence
- 1.5 Analytics Overview
- Descriptive Analytics
- Application Case 1.3 An Post and the Use of Data Visualization in Daily Postal Operations
- Application Case 1.4 Siemens Reduces Cost with the Use of Data Visualization
- Predictive Analytics
- Application Case 1.5 SagaDigits and the Use of Predictive Analytics
- Prescriptive Analytics
- Application Case 1.6 A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise
- 1.6 Analytics Examples in Selected Domains
- Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics
- Analytics Applications in Healthcare—Humana Examples
- Application Case 1.7 Image Analysis Helps Estimate Plant Cover
- 1.7 Artificial Intelligence Overview
- What Is Artificial Intelligence
- The Major Benefits of AI
- The Landscape of AI
- Application Case 1.8 AI Increases Passengers’ Comfort and Security in Airports and Borders
- The Three Flavors of AI Decisions
- Autonomous AI
- Societal Impacts
- Application Case 1.9 Robots Took the Job of Camel-Racing Jockeys for Societal Benefits
- 1.8 Convergence of Analytics and AI
- Major Differences between Analytics and AI
- Why Combine Intelligent Systems
- How Convergence Can Help
- Big Data Is Empowering AI Technologies
- The Convergence of AI and the IoT
- The Convergence with Blockchain and Other Technologies
- Application Case 1.10 Amazon Go Is Open for Business
- IBM and Microsoft Support for Intelligent Systems Convergence
- 1.9 Overview of the Analytics Ecosystem
- 1.10 Plan of the Book
- 1.11 Resources, Links, and the Teradata University Network Connection
- Resources and Links
- Vendors, Products, and Demos
- Periodicals
- The Teradata University Network Connection
- The Book’s Web Site
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications
- 2.1 Opening Vignette: INRIX Solves Transportation Problems
- 2.2 Introduction to Artificial Intelligence
- Definitions
- Major Characteristics of AI Machines
- Major Elements of AI
- AI Applications
- Major Goals of AI
- Drivers of AI
- Benefits of AI
- Some Limitations of AI Machines
- Three Flavors of AI Decisions
- Artificial Brain
- 2.3 Human and Computer Intelligence
- What Is Intelligence
- How Intelligent Is AI
- Measuring AI
- Application Case 2.1 How Smart Can a Vacuum Cleaner Be
- 2.4 Major AI Technologies and Some Derivatives
- Intelligent Agents
- Machine Learning
- Application Case 2.2 How Machine Learning Is Improving Work in Business
- Machine and Computer Vision
- Robotic Systems
- Natural Language Processing
- Knowledge and Expert Systems and Recommenders
- Chatbots
- Emerging AI Technologies
- 2.5 AI Support for Decision Making
- Some Issues and Factors in Using AI in Decision Making
- AI Support of the Decision-Making Process
- Automated Decision Making
- Application Case 2.3 How Companies Solve Real-World Problems Using Google’s Machine-Learning Tools
- Conclusion
- 2.6 AI Applications in Accounting
- AI in Accounting: An Overview
- AI in Big Accounting Companies
- Accounting Applications in Small Firms
- Application Case 2.4 How EY, Deloitte, and PwC Are Using AI
- Job of Accountants
- 2.7 AI Applications in Financial Services
- AI Activities in Financial Services
- AI in Banking: An Overview
- Illustrative AI Applications in Banking
- Insurance Services
- Application Case 2.5 AI in China’s Financial Sector
- 2.8 AI in Human Resource Management (HRM)
- AI in HRM: An Overview
- AI in Onboarding
- Application Case 2.6 How Alexander Mann Solutions (AMS) Is Using AI to Support the Recruiting Proces
- Introducing AI to HRM Operations
- 2.9 AI in Marketing, Advertising, and CRM
- Overview of Major Applications
- AI Marketing Assistants in Action
- Customer Experiences and CRM
- Application Case 2.7 Kraft Foods Uses AI for Marketing and CRM
- Other Uses of AI in Marketing
- 2.10 AI Applications in Production-Operation Management (POM)
- AI in Manufacturing
- Implementation Model
- Intelligent Factories
- Logistics and Transportation
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 3 Nature of Data, Statistical Modeling, and Visualization
- 3.1 Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Dr
- 3.2 Nature of Data
- 3.3 Simple Taxonomy of Data
- Application Case 3.1 Verizon Answers the Call for Innovation: The Nation’s Largest Network Provide
- 3.4 Art and Science of Data Preprocessing
- Application Case 3.2 Improving Student Retention with Data-Driven Analytics
- 3.5 Statistical Modeling for Business Analytics
- Descriptive Statistics for Descriptive Analytics
- Measures of Centrality Tendency (Also Called Measures of Location or Centrality)
- Arithmetic Mean
- Median
- Mode
- Measures of Dispersion (Also Called Measures of Spread or Decentrality)
- Range
- Variance
- Standard Deviation
- Mean Absolute Deviation
- Quartiles and Interquartile Range
- Box-and-Whiskers Plot
- Shape of a Distribution
- Application Case 3.3 Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and De
- 3.6 Regression Modeling for Inferential Statistics
- How Do We Develop the Linear Regression Model
- How Do We Know If the Model Is Good Enough
- What Are the Most Important Assumptions in Linear Regression
- Logistic Regression
- Time-Series Forecasting
- Application Case 3.4 Predicting NCAA Bowl Game Outcomes
- 3.7 Business Reporting
- Application Case 3.5 Flood of Paper Ends at FEMA
- 3.8 Data Visualization
- Brief History of Data Visualization
- Application Case 3.6 Macfarlan Smith Improves Operational Performance Insight with Tableau Online
- 3.9 Different Types of Charts and Graphs
- Basic Charts and Graphs
- Specialized Charts and Graphs
- Which Chart or Graph Should You Use
- 3.10 Emergence of Visual Analytics
- Visual Analytics
- High-Powered Visual Analytics Environments
- 3.11 Information Dashboards
- Application Case 3.7 Flink Labs and Dashboard Applications Development
- Dashboard Design
- Application Case 3.8 Visual Analytics Helps Energy Supplier Make Better Connections
- What to Look for in a Dashboard
- Best Practices in Dashboard Design
- Benchmark Key Performance Indicators with Industry Standards
- Wrap the Dashboard Metrics with Contextual Metadata
- Validate the Dashboard Design by a Usability Specialist
- Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard
- Enrich the Dashboard with Business-User Comments
- Present Information in Three Different Levels
- Pick the Right Visual Construct Using Dashboard Design Principles
- Provide for Guided Analytics
- Chapter Highlights
- Key terms
- Questions for Discussion
- Exercises
- References
- Chapter 1 Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence:
- Chapter 4 Data Mining Process, Methods, and Algorithms
- 4.1 Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Figh
- 4.2 Data Mining Concepts
- Application Case 4.1 Visa Is Enhancing the Customer Experience While Reducing Fraud with Predictive
- Definitions, Characteristics, and Benefits
- How Data Mining Works
- Application Case 4.2 American Honda Uses Advanced Analytics to Improve Warranty Claims
- Data Mining versus Statistics
- 4.3 Data Mining Applications
- Application Case 4.3 Predictive Analytic and Data Mining Help Stop Terrorist Funding
- 4.4 Data Mining Process
- Step 1: Business Understanding
- Step 2: Data Understanding
- Step 3: Data Preparation
- Step 4: Model Building
- Application Case 4.4 Data Mining Helps in Cancer Research
- Step 5: Testing and Evaluation
- Step 6: Deployment
- Other Data Mining Standardized Processes and Methodologies
- 4.5 Data Mining Methods
- Classification
- Estimating the True Accuracy of Classification Models
- Estimating the Relative Importance of Predictor Variables
- Cluster Analysis for Data Mining
- Application Case 4.5 Influence Health Uses Advanced Predictive Analytics to Focus on the Factors Tha
- Association Rule Mining
- 4.6 Data Mining Software Tools
- Application Case 4.6 Data Mining Goes to Hollywood: Predicting Financial Success of Movies
- 4.7 Data Mining Privacy Issues, Myths, and Blunders
- Application Case 4.7 Predicting Customer Buying Patterns—The Target Story
- Data Mining Myths and Blunders
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 5 Machine-Learning Techniques for Predictive Analytics
- 5.1 Opening Vignette: Predictive Modeling Helps Better Understand and Manage Complex Medical Procedu
- 5.2 Basic Concepts of Neural Networks
- Biological versus Artificial Neural Networks
- Application Case 5.1 Neural Networks Are Helping to Save Lives in the Mining Industry
- 5.3 Neural Network Architectures
- Kohonen’s Self-Organizing Feature Maps
- Hopfield Networks
- Application Case 5.2 Predictive Modeling Is Powering the Power Generators
- 5.4 Support Vector Machines
- Application Case 5.3 Identifying Injury Severity Risk Factors in Vehicle Crashes with Predictive Ana
- Mathematical Formulation of SVM
- Primal Form
- Dual Form
- Soft Margin
- Nonlinear Classification
- Kernel Trick
- 5.5 Process-Based Approach to the Use of SVM
- Support Vector Machines versus Artificial Neural Networks
- 5.6 Nearest Neighbor Method for Prediction
- Similarity Measure: The Distance Metric
- Parameter Selection
- Application Case 5.4 Efficient Image Recognition and Categorization with knn
- 5.7 Naïve Bayes Method for Classification
- Bayes Theorem
- Naïve Bayes Classifier
- Process of Developing a Naïve Bayes Classifier
- Testing Phase
- Application Case 5.5 Predicting Disease Progress in Crohn’s Disease Patients: A Comparison of Anal
- 5.8 Bayesian Networks
- How Does BN Work
- How Can BN Be Constructed
- 5.9 Ensemble Modeling
- Motivation—Why Do We Need to Use Ensembles
- Different Types of Ensembles
- Bagging
- Boosting
- Variants of Bagging and Boosting
- Stacking
- Information Fusion
- Summary—Ensembles Are Not Perfect
- Application Case 5.6 To Imprison or Not to Imprison: A Predictive Analytics–Based Decision Support
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- Internet Exercises
- References
- Chapter 6 Deep Learning and Cognitive Computing
- 6.1 Opening Vignette: Fighting Fraud with Deep Learning and Artificial Intelligence
- 6.2 Introduction to Deep Learning
- Application Case 6.1 Finding the Next Football Star with Artificial Intelligence
- 6.3 Basics of “Shallow” Neural Networks
- Application Case 6.2 Gaming Companies Use Data Analytics to Score Points with Players
- Application Case 6.3 Artificial Intelligence Helps Protect Animals from Extinction
- 6.4 Process of Developing Neural Network–Based Systems
- Learning Process in ANN
- Backpropagation for ANN Training
- 6.5 Illuminating the Black Box of ANN
- Application Case 6.4 Sensitivity Analysis Reveals Injury Severity Factors in Traffic Accidents
- 6.6 Deep Neural Networks
- Feedforward Multilayer Perceptron (MLP)-Type Deep Networks
- Impact of Random Weights in Deep MLP
- More Hidden Layers versus More Neurons
- Application Case 6.5 Georgia DOT Variable Speed Limit Analytics Help Solve Traffic Congestions
- 6.7 Convolutional Neural Networks
- Convolution Function
- Pooling
- Image Processing Using Convolutional Networks
- Application Case 6.6 From Image Recognition to Face Recognition
- Text Processing Using Convolutional Networks
- 6.8 Recurrent Networks and Long Short-Term Memory Networks
- Application Case 6.7 Deliver Innovation by Understanding Customer Sentiments
- LSTM Networks Applications
- 6.9 Computer Frameworks for Implementation of Deep Learning
- Torch
- Caffe
- TensorFlow
- Theano
- Keras: An Application Programming Interface
- 6.10 Cognitive Computing
- How Does Cognitive Computing Work
- How Does Cognitive Computing Differ from AI
- Cognitive Search
- IBM Watson: Analytics at Its Best
- Application Case 6.8 IBM Watson Competes against the Best at Jeopardy
- How Does Watson Do It
- What Is the Future for Watson
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 7 Text Mining, Sentiment Analysis, and Social Analytics
- 7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into Near-Real-Time Sales
- 7.2 Text Analytics and Text Mining Overview
- Application Case 7.1 Netflix: Using Big Data to Drive Big Engagement: Unlocking the Power of Analyti
- 7.3 Natural Language Processing (NLP)
- Application Case 7.2 AMC Networks Is Using Analytics to Capture New Viewers, Predict Ratings, and Ad
- 7.4 Text Mining Applications
- Marketing Applications
- Security Applications
- Biomedical Applications
- Application Case 7.3 Mining for Lies
- Academic Applications
- Application Case 7.4 The Magic behind the Magic: Instant Access to Information Helps the Orlando Mag
- 7.5 Text Mining Process
- Task 1: Establish the Corpus
- Task 2: Create the Term–Document Matrix
- Task 3: Extract the Knowledge
- Application Case 7.5 Research Literature Survey with Text Mining
- 7.6 Sentiment Analysis
- Application Case 7.6 Creating a Unique Digital Experience to Capture Moments That Matter at Wimbledo
- Sentiment Analysis Applications
- Sentiment Analysis Process
- Methods for Polarity Identification
- Using a Lexicon
- Using a Collection of Training Documents
- Identifying Semantic Orientation of Sentences and Phrases
- Identifying Semantic Orientation of Documents
- 7.7 Web Mining Overview
- Web Content and Web Structure Mining
- 7.8 Search Engines
- Anatomy of a Search Engine
- 1. Development Cycle
- 2. Response Cycle
- Search Engine Optimization
- Methods for Search Engine Optimization
- Application Case 7.7 Delivering Individualized Content and Driving Digital Engagement: How Barbour C
- 7.9 Web Usage Mining (Web Analytics)
- Web Analytics Technologies
- Web Analytics Metrics
- Web Site Usability
- Traffic Sources
- Visitor Profiles
- Conversion Statistics
- 7.10 Social Analytics
- Social Network Analysis
- Social Network Analysis Metrics
- Application Case 7.8 Tito’s Vodka Establishes Brand Loyalty with an Authentic Social Strategy
- Connections
- Distributions
- Segmentation
- Social Media Analytics
- How Do People Use Social Media
- Measuring the Social Media Impact
- Best Practices in Social Media Analytics
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 8 Prescriptive Analytics: Optimization and Simulation
- 8.1 Opening Vignette: School District of Philadelphia Uses Prescriptive Analytics to Find Optimal So
- 8.2 Model-Based Decision Making
- Application Case 8.1 Canadian Football League Optimizes Game Schedule
- Prescriptive Analytics Model Examples
- Identification of the Problem and Environmental Analysis
- Application Case 8.2 Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions
- Model Categories
- 8.3 Structure of Mathematical Models for Decision Support
- The Components of Decision Support Mathematical Models
- The Structure of Mathematical Models
- 8.4 Certainty, Uncertainty, and Risk
- Decision Making under Certainty
- Decision Making under Uncertainty
- Decision Making under Risk (Risk Analysis)
- Application Case 8.3 American Airlines Uses Should-Cost Modeling to Assess the Uncertainty of Bids f
- 8.5 Decision Modeling with Spreadsheets
- Application Case 8.4 Pennsylvania Adoption Exchange Uses Spreadsheet Model to Better Match Children
- Application Case 8.5 Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Route
- 8.6 Mathematical Programming Optimization
- Application Case 8.6 Mixed-Integer Programming Model Helps the University of Tennessee Medical Cente
- Linear Programming Model
- Modeling in LP: An Example
- Implementation
- 8.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
- Multiple Goals
- Sensitivity Analysis
- What-If Analysis
- Goal Seeking
- 8.8 Decision Analysis with Decision Tables and Decision Trees
- Decision Tables
- Decision Trees
- 8.9 Introduction to Simulation
- Major Characteristics of Simulation
- Application Case 8.7 Steel Tubing Manufacturer Uses a Simulation-Based Production Scheduling System
- Advantages of Simulation
- Disadvantages of Simulation
- The Methodology of Simulation
- Simulation Types
- Monte Carlo Simulation
- Discrete Event Simulation
- Application Case 8.8 Cosan Improves Its Renewable Energy Supply Chain Using Simulation
- 8.10 Visual Interactive Simulation
- Conventional Simulation Inadequacies
- Visual Interactive Simulation
- Visual Interactive Models and DSS
- Simulation Software
- Application Case 8.9 Improving Job-Shop Scheduling Decisions through RFID: A Simulation-Based Assess
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 9 Big Data, Cloud Computing, and Location Analytics: Concepts and Tools
- 9.1 Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data Methods
- 9.2 Definition of Big Data
- The “V”s That Define Big Data
- Application Case 9.1 Alternative Data for Market Analysis or Forecasts
- 9.3 Fundamentals of Big Data Analytics
- Business Problems Addressed by Big Data Analytics
- Application Case 9.2 Big Data and Retail Business: The Rise of ABEJA
- 9.4 Big Data Technologies
- MapReduce
- Why Use MapReduce
- Hadoop
- How Does Hadoop Work
- Hadoop Technical Components
- Hadoop: The Pros and Cons
- NoSQL
- Application Case 9.3 eBay’s Big Data Solution
- Application Case 9.4 Understanding Quality and Reliability of Healthcare Support Information on Twit
- 9.5 Big Data and Data Warehousing
- Use Cases for Hadoop
- Use Cases for Data Warehousing
- The Gray Areas (Any One of the Two Would Do the Job)
- Coexistence of Hadoop and Data Warehouse
- 9.6 In-Memory Analytics and Apache Spark
- Application Case 9.5 Databrick’s Apache SparkTM: Asia-Pacific Big Data Processing in Action
- Architecture of Apache SparkTM
- Getting Started with Apache SparkTM
- 9.7 Big Data and Stream Analytics
- Stream Analytics versus Perpetual Analytics
- Critical Event Processing
- Data Stream Mining
- Applications of Stream Analytics
- e-Commerce
- Telecommunications
- Application Case 9.6 Salesforce Is Using Streaming Data to Enhance Customer Value
- Law Enforcement and Cybersecurity
- Power Industry
- Financial Services
- Health Sciences
- Government
- 9.8 Big Data Vendors and Platforms
- Infrastructure Services Providers
- Analytics Solution Providers
- Business Intelligence Providers Incorporating Big Data
- Application Case 9.7 Using Social Media for Nowcasting Flu Activity
- Application Case 9.8 Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse
- 9.9 Cloud Computing and Business Analytics
- Data as a Service (DaaS)
- Software as a Service (SaaS)
- Platform as a Service (PaaS)
- Infrastructure as a Service (IaaS)
- Essential Technologies for Cloud Computing
- Application Case 9.9 Major West Coast Utility Uses Cloud-Mobile Technology to Provide Real-Time Inci
- Cloud Deployment Models
- Major Cloud Platform Providers in Analytics
- Analytics as a Service (AaaS)
- Representative Analytics as a Service Offerings
- Illustrative Analytics Applications Employing the Cloud Infrastructure
- Using Azure IOT, Stream Analytics, and Machine Learning to Improve Mobile Health Care Services
- Gulf Air Uses Big Data to Get Deeper Customer Insight
- Chime Enhances Customer Experience Using Snowflake
- 9.10 Location-Based Analytics for Organizations
- Geospatial Analytics
- Application Case 9.10 GIS and the Indian Retail Industry
- Application Case 9.11 Starbucks Exploits GIS and Analytics to Grow Worldwide
- Real-Time Location Intelligence
- Analytics Applications for Consumers
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 10 Robotics: Industrial and Consumer Applications
- 10.1 Opening Vignette: Robots Provide Emotional Support to Patients and Children
- 10.2 Overview of Robotics
- 10.3 History of Robotics
- 10.4 Illustrative Applications of Robotics
- Changing Precision Technology
- Adidas
- BMW Employs Collaborative Robots
- Tega
- San Francisco Burger Eatery
- Spyce
- Mahindra & Mahindra Ltd
- Robots in the Defense Industry
- Pepper
- Da Vinci Surgical System
- Snoo–A Robotic Crib
- MEDi
- Care-E Robot
- AGROBOT
- 10.5 Components of Robots
- 10.6 Various Categories of Robots
- 10.7 Autonomous Cars: Robots in Motion
- Autonomous Vehicle Development
- Issues with Self-Driving Cars
- 10.8 Impact of Robots on Current and Future Jobs
- 10.9 Legal Implications of Robots and Artificial Intelligence
- Tort Liability
- Patents
- Property
- Taxation
- Practice of Law
- Constitutional Law
- Professional Certification
- Law Enforcement
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 11 Group Decision Making, Collaborative Systems, and AI Support
- 11.1 Opening Vignette: Hendrick Motorsports Excels with Collaborative Teams
- 11.2 Making Decisions in Groups: Characteristics, Process, Benefits, and Dysfunctions
- Characteristics of Group Work
- Types of Decisions Made by Groups
- Group Decision-Making Process
- Benefits and Limitations of Group Work
- 11.3 Supporting Group Work and Team Collaboration with Computerized Systems
- Overview of Group Support Systems (GSS)
- Time/Place Framework
- Group Collaboration for Decision Support
- 11.4 Electronic Support for Group Communication and Collaboration
- Groupware for Group Collaboration
- Synchronous versus Asynchronous Products
- Virtual Meeting Systems
- Collaborative Networks and Hubs
- Collaborative Hubs
- Social Collaboration
- Sample of Popular Collaboration Software
- 11.5 Direct Computerized Support for Group Decision Making
- Group Decision Support Systems (GDSS)
- Characteristics of GDSS
- Supporting the Entire Decision-Making Process
- Brainstorming for Idea Generation and Problem Solving
- Group Support Systems
- 11.6 Collective Intelligence and Collaborative Intelligence
- Definitions and Benefits
- Computerized Support to Collective Intelligence
- Application Case 11.1 Collaborative Modeling for Optimal Water Management: The Oregon State Universi
- How Collective Intelligence May Change Work and Life
- Collaborative Intelligence
- How to Create Business Value from Collaboration: The IBM Study
- 11.7 Crowdsourcing as a Method for Decision Support
- The Essentials of Crowdsourcing
- Crowdsourcing for Problem-Solving and Decision Support
- Implementing Crowdsourcing for Problem Solving
- Application Case 11.2 How InnoCentive Helped GSK Solve a Difficult Problem
- 11.8 Artificial Intelligence and Swarm AI Support of Team Collaboration and Group Decision Making
- AI Support of Group Decision Making
- AI Support of Team Collaboration
- Swarm Intelligence and Swarm AI
- Application Case 11.3 XPRIZE Optimizes Visioneering
- 11.9 Human–Machine Collaboration and Teams of Robots
- Human–Machine Collaboration in Cognitive Jobs
- Robots as Coworkers: Opportunities and Challenges
- Teams of collaborating Robots
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 12 Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, a
- 12.1 Opening Vignette: Sephora Excels with Chatbots
- 12.2 Expert Systems and Recommenders
- Basic Concepts of Expert Systems (ES)
- Characteristics and Benefits of ES
- Typical Areas for ES Applications
- Structure and Process of ES
- Application Case 12.1 ES Aid in Identification of Chemical, Biological, and Radiological Agents
- Why the Classical Type of ES Is Disappearing
- Application Case 12.2 VisiRule
- Recommendation Systems
- Application Case 12.3 Netflix Recommender: A Critical Success Factor
- 12.3 Concepts, Drivers, and Benefits of Chatbots
- What Is a Chatbot
- Chatbot Evolution
- Components of Chatbots and the Process of Their Use
- Drivers and Benefits
- Representative Chatbots from around the World
- 12.4 Enterprise Chatbots
- The Interest of Enterprises in Chatbots
- Enterprise Chatbots: Marketing and Customer Experience
- Application Case 12.4 WeChat’s Super Chatbot
- Application Case 12.5 How Vera Gold Mark Uses Chatbots to Increase Sales
- Enterprise Chatbots: Financial Services
- Enterprise Chatbots: Service Industries
- Chatbot Platforms
- Application Case 12.6 Transavia Airlines Uses Bots for Communication and Customer Care Delivery
- Knowledge for Enterprise Chatbots
- 12.5 Virtual Personal Assistants
- Assistant for Information Search
- If You Were Mark Zuckerberg, Facebook CEO
- Amazon’s Alexa and Echo
- Apple’s Siri
- Google Assistant
- Other Personal Assistants
- Competition among Large Tech Companies
- Knowledge for Virtual Personal Assistants
- 12.6 Chatbots as Professional Advisors (Robo Advisors)
- Robo Financial Advisors
- Evolution of Financial Robo Advisors
- Robo Advisors 2.0: Adding the Human Touch
- Application Case 12.7 Barclays: AI and Chatbots in Banking
- Managing Mutual Funds Using AI
- Other Professional Advisors
- IBM Watson
- 12.7 Implementation Issues
- Technology Issues
- Disadvantages and Limitations of Bots
- Quality of Chatbots
- Setting Up Alexa’s Smart Home System
- Constructing Bots
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 13 The Internet of Things as a Platform for Intelligent Applications
- 13.1 Opening Vignette: CNH Industrial Uses the Internet of Things to Excel
- 13.2 Essentials of IoT
- Definitions and Characteristics
- The IoT Ecosystem
- Structure of IoT Systems
- 13.3 Major Benefits and Drivers of IoT
- Major Benefits of IoT
- Major Drivers of IoT
- Opportunities
- 13.4 How IoT Works
- IoT and Decision Support
- 13.5 Sensors and Their Role in IoT
- Brief Introduction to Sensor Technology
- Application Case 13.1 Using Sensors, IoT, and AI for Environmental Control at the Athens Internation
- How Sensors Work with IoT
- Application Case 13.2 Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets to Predi
- Sensor Applications and Radio-Frequency Identification (RFID) Sensors
- 13.6 Selected IoT Applications
- A Large-Scale IoT in Action
- Examples of Other Existing Applications
- 13.7 Smart Homes and Appliances
- Typical Components of Smart Homes
- Smart Appliances
- A Smart Home Is Where the Bot Is
- Barriers to Smart Home Adoption
- 13.8 Smart Cities and Factories
- Application Case 13.3 Amsterdam on the Road to Become a Smart City
- Smart Buildings: From Automated to Cognitive Buildings
- Smart Components in Smart Cities and Smart Factories
- Application Case 13.4 How IBM Is Making Cities Smarter Worldwide
- Improving Transportation in the Smart City
- Combining Analytics and IoT in Smart City Initiatives
- Bill Gates’ Futuristic Smart City
- Technology Support for Smart Cities
- 13.9 Autonomous (Self-Driving) Vehicles
- The Developments of Smart Vehicles
- Application Case 13.5 Waymo and Autonomous Vehicles
- Flying Cars
- Implementation Issues in Autonomous Vehicles
- 13.10 Implementing IoT and Managerial Considerations
- Major Implementation Issues
- Strategy for Turning Industrial IoT into Competitive Advantage
- The Future of the IoT
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 14 Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts
- 14.1 Opening Vignette: Why Did Uber Pay $245 Million to Waymo
- 14.2 Implementing Intelligent Systems: An Overview
- The Intelligent Systems Implementation Process
- The Impacts of Intelligent Systems
- 14.3 Legal, Privacy, and Ethical Issues
- Legal Issues
- Privacy Issues
- Who Owns Our Private Data
- Ethics Issues
- Ethical Issues of Intelligent Systems
- Other Topics in Intelligent Systems Ethics
- 14.4 Successful Deployment of Intelligent Systems
- Top Management and Implementation
- System Development Implementation Issues
- Connectivity and Integration
- Security Protection
- Leveraging Intelligent Systems in Business
- Intelligent System Adoption
- 14.5 Impacts of Intelligent Systems on Organizations
- New Organizational Units and Their Management
- Transforming Businesses and Increasing Competitive Advantage
- Application Case 14.1 How 1-800-Flowers.com Uses Intelligent Systems for Competitive Advantage
- Redesign of an Organization through the Use of Analytics
- Intelligent Systems’ Impact on Managers’ Activities, Performance, and Job Satisfaction
- Impact on Decision Making
- Industrial Restructuring
- 14.6 Impacts on Jobs and Work
- An Overview
- Are Intelligent Systems Going to Take Jobs—My Job
- AI Puts Many Jobs at Risk
- Application Case 14.2 White-Collar Jobs That Robots Have Already Taken
- Which Jobs Are Most in Danger? Which Ones Are Safe
- Intelligent Systems May Actually Add Jobs
- Jobs and the Nature of Work Will Change
- Conclusion: Let’s Be Optimistic
- 14.7 Potential Dangers of Robots, AI, and Analytical Modeling
- Position of AI Dystopia
- The AI Utopia’s Position
- The Open AI Project and the Friendly AI
- The O’Neil Claim of Potential Analytics’ Dangers
- 14.8 Relevant Technology Trends
- Gartner’s Top Strategic Technology Trends for 2018 and 2019
- Other Predictions Regarding Technology Trends
- Summary: Impact on AI and Analytics
- Ambient Computing (Intelligence)
- 14.9 Future of Intelligent Systems
- What Are the Major U.S. High-Tech Companies Doing in the Intelligent Technologies Field
- AI Research Activities in China
- Application Case 14.3 How Alibaba.com Is Conducting AI
- The U.S.–China Competition: Who Will Control AI
- The Largest Opportunity in Business
- Conclusion
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
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