Decision support and business intelligence systems/ Efraim Turban

Part I: Decision Support and Business Intelligence1. Decision Support Systems and Business Intelligence1.1 Opening Vignette: Toyota Uses Business Intelligence to Excel1.2 Changing Business Environments and Computerized Decision Support1.3 Managerial Decision Making1.4 Computerized Support for Decision Making1.5 An Early Framework for Computerized Decision Support1.6 The Concept of Decision Support Systems (DSS)1.7 A Framework for Business Intelligence (BI)1.8 A Work System View of Decision Support1.9 The Major Tools and Techniques of Managerial Decision Support1.10 Implementing Computer-Based Managerial Decision Support Systems1.11 Plan of the Book1.12 Resources, Links, and the Teradata University Network Connection

Part II: Computerized Decision Support2. Decision Making, Systems, Modeling, and Support2.1Opening Vignette: Decision Making at the U.S. Federal Reserve2.2 Decision Making: Introduction and Definitions2.3 Models2.4 Phases of the Decision Making Process2.5 Decision Making: The Intelligence Phase2.6 Decision Making: The Design Phase2.7 Decision Making: The Choice Phase2.8 Decision Making: The Implementation Phase2.9 How Decisions are Supported2.10 Resources, Links, and the Teradata University Network Connection 3. Decision Support Systems Concepts, Methodologies, and Technologies: An Overview3.1 Opening Vignette: Decision Support System Cures for Healthcare3.2 DSS Configurations3.3 DSS Description3.4 DSS Characteristics and Capabilities3.5 Components of DSS3.6 The Data Management Subsystem3.7 The Model Management Subsystem3.8 The User Interface (Dialog) Subsystem3.9 The Knowledge-Based Management Subsystem3.10 The User3.11 DSS Hardware3.12 DSS Classifications 4. Modeling and Analysis4.1 Opening Vignette: "Winning Isn't Everything... But Losing Isn't Anything:" Professional Sports Modeling for Decision Making4.2 MSS Modeling4.3 Static and Dynamic Models4.4 Certainty, Uncertainty, and Risk4.5 MSS Modeling with Spreadsheets4.6 Decision Analysis with Decision Tables and Decision Trees4.7 The Structure of Mathematical Models for Decision Support4.8 Mathematical Programming Optimization4.9 Multiple Goals, Sensitivity Analysis, What-IF, and Goal Seeking4.10 Problem Solving Search Methods4.11 Simulation4.12 Visual Interactive Simulation4.13 Quantitative Software Packages and Model Base Management4.14 Resources, Links, and the Teradata University Network Connection

Part III: Business IntelligenceSpecial Introductory Section: The Essentials of Business Intelligence1.1 A Preview of the Content of Chapters 5-9S.2 The Origins and Drivers of Business IntelligenceS.3 The General Process of Intelligence Creation and UseS.4 The Major Characteristics of Business IntelligenceS.5 Towards Competitive Intelligence and AdvantageS.6 The Typical Data Warehouse and BI User CommunityS.7 Successful BI ImplementationS.8 Structure and Components of BIS.9 Conclusion: Today and Tomorrow 5. Data Warehousing5.1 Opening Vignette: Continental Airlines Flies High with Its Real-Time Data Warehouse5.2 Data Warehousing Definitions and Concepts5.3 Data Warehousing Process Overview5.4 Data Warehousing Architectures5.5 Data Integration, and the Extraction, Transformation, and Load (ETL) Process5.6 Data Warehouse Development5.7 Real-Time Data Warehouses5.8 Data Warehouse Administration and Security Issues 6. Business Analytics and Data Visualization6.1 Opening Vignette: Lexmark International Improves Operations with BI6.2 The Business Analytics Field-An Overview6.3 Online Analytical Processing (OLAP)6.4 Reporting and Queries6.5 Multidimensionality6.6 Advanced Business Analytics6.7 Data Visualization6.8 Geographic Information Systems6.9 Real-Time Business Intelligence, Automated Decision Support, and Competitive Intelligence6.10 Business Analytics and the Web: Web Intelligence and Web Analytics6.11 Usage, Benefits, and Success of Business Analytics 7. Data, Text, and Web Mining7.1 Opening Vignette: Highmark, Inc.7.2 Data Mining Concepts and Applications7.3 Data Mining Techniques and Tools7.4 Data Mining Project Process7.5 Text Mining7.6 Web Mining 8. Neural Networks for Data Mining8.1 Opening Vignette: Using Neural Networks to Predict Beer Flavors From Chemical Analysis8.2 Basic Concepts of Neural Networks8.3 Learning in Artificial Neural Networks8.4 Developing Neural Network Systems8.5 A Sample Neural Network Project8.6 Other Neural Networks Paradigms8.7 Applications of Neural Networks8.8 A Neural Network Software Demonstration 9. Business Performance Management9.1 Opening Vignette: Cisco and the Virtual Close9.2 Business Performance Management Overview9.3 Strategize: Where Do We Want to Go?9.4 Plan: How Do We Get There?9.5 Monitor: How are We Doing?9.6 Act and Adjust: What Do We Need to Do Differently?9.7 Performance Measurement9.8 Bpm Methodologies9.9 Bpm Architecture and Applications9.10 Performance Dashboards9.11 Business Activity Monitoring (BAM)

Part IV: Collaboration, Communication, Group Support Systems, and Knowledge Management10. Collaborative Computing-Supported Technologies and Group Support Systems10.1 Opening Vignette: Collaborative Design at Boein-Rocketdyne10.2 Making Decisions in Groups: Characteristics, Process, Benefits, and Dysfunctions10.3 Supporting Groupwork with Computerized Systems10.4 Tools for Indirect Support of Decision Making10.5 Integrated Groupware Suites10.6 Direct Computerized Support for Decision Making: From GDSS to GSS10.7 Products and Tools for GDSS/GSS and Successful Implementation10.8 Emerging Collaboration Support Tools: From VoIP to Wikis10.9 Collaborative Efforts in Planning, Design, and the Project Management10.10 Creativity, Idea Generation and Computerized Support 11. Knowledge Management11.1 Opening Vignette: Simens Knows What It Knows through Knowledge Management11.2 Introduction to Knowledge Management11.3 Organizational Learning and Transformation11.4 Knowlege Management Activities11.5 Approached to Knowledge Management11.6 Information Technology in Knowledge Management11.7 Knowledge Management Systems Implementation11.8 Roles of People in Knowledge Management11.9 Ensuring the Success of Knowledge Management Efforts

Part V: Intelligent Systems12. Artificial Intelligence and Expert Systems12. 1 Opening Vignette: Cigna Uses Business Rules to Support Treatment Request Approval12.2 Concepts and Definition of Artificial Intelligence12.3 The Artificial Intelligence Fields12.4 Basic Concepts of Expert Systems12.5 Applications of Expert Systems12.6 Structure of Expert Systems12.7 How Expert Systems Work- Inference Mechanisms12.8 Problem Areas Suitable for Expert Systems12.9 Development of Expert Systems12.10 Benefits, Limitations and Success Factors of Expert Systems12.11 Expert Systems on the Web 13. Advanced Intelligent Systems13. 1 Opening Vignette: Improving Urban Infrastructure Management in the City of Verdum13.2 Machine Learning Techniques13. 3 Case-based Reasoning13.4 Genetic Algorithms Fundamentals13.5 Developing Genetic Algorithm Applications13.6 Fuzzy Logic Fundamentals13.7 Natural Language Processing13.8 Voice Technologies13.9 Developing Integrated Advanced System 14. Intelligent Systems over the Internet14.1 Opening Vignette: NetFlix Gains High Customer Satisfaction from DVD Recommendation14.2 Web-Based Intelligent Systems14.3 Intelligent Agents: An Overview14.4 Characteristics of Intelligent Agents14.5 Why Use Intelligent Agents14.6 Classification and Types of Intelligent Agents14.7 Internet-Based Software Agents14.8 DSS Agents and Multi-agents14.9 Semantic Web: Representing Knowledge for Intelligent Agents14.10 Web-Cased Recommendation Systems14.11 Managerial Issues of Intelligent Agents

Part VI: Implementing Decision Support Systems15. Systems Development and Acquisition15.1 Opening Vignette: Osram Sylvania Thinks Small, Strategizes Big- Develops the InfoNet HR Portal System15.2 What Types of Support Systems Should We Build?15.3 The Landscape and Framework of MSS Applications Development15.4 Development Options for MSS Applications15.5 Prototyping: A Practical MSS Development Methodology15.6 Criteria For Selecting a Development Approach15.7 Third-Party Providers of MSS Software Packages and Suites15.8 Connecting to Databases and Other Enterprise Systems15.9 Rise of Web Services, XML, and Service-Oriented Architecture15.10 End-user Developed MSS15.11 Vendor and Software Selection and Management15.12 Putting the MSS Together and Implementation Issues 16. Integration, Impacts, and the Future of Management Support Systems16.1 Opening Vignette: Elite-Care Supported by Intelligent Systems16.2 Systems Integration: An Overview16.3 Types of MSS Integration16.4 Integration with Enterprise Systems and Knowledge Management16.5 The Impacts of MSS: An Overview16.6 MSS Impacts on Organizations16.7 MSS Impacts on Individuals16.8 Automating Decision Making and the Manager's Job16.9 Issues of Legality, Privacy, and Ethics16.10 Intelligent and Automated Systems and Employment Levels16.11 Other Societal Impacts and the Digital Divide16.12 The Future of Management Support Systems16.13 Resources, Links, and Teradata University Connection