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Integration of Data Mining in Business Intelligence Systems

Book Description

Uncovering and analyzing data associated with the current business environment is essential in maintaining a competitive edge. As such, making informed decisions based on this data is crucial to managers across industries. Integration of Data Mining in Business Intelligence Systems investigates the incorporation of data mining into business technologies used in the decision making process. Emphasizing cutting-edge research and relevant concepts in data discovery and analysis, this book is a comprehensive reference source for policymakers, academicians, researchers, students, technology developers, and professionals interested in the application of data mining techniques and practices in business information systems.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
  5. Dedication
  6. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  7. Foreword
  8. Preface
    1. STRUCTURE OF THE BOOK
    2. CONCLUSION
    3. REFERENCES
  9. Acknowledgment
  10. Section 1: Fundamentals and Literature Review
    1. Chapter 1: Data Mining and Business Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. BUSINESS INTELLIGENCE
      4. DATA MINING
      5. BUSINESS INTELLIGENCE AND DATA MINING
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
      11. ENDNOTES
    2. Chapter 2: The Role of Data Mining for Business Intelligence in Knowledge Management
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE ROLE OF DATA MINING FOR BUSINESS INTELLIGENCE IN KNOWLEDGE MANAGEMENT
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    3. Chapter 3: Data Quality for Data Mining in Business Intelligence Applications
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. APPROACHES AND FRAMEWORKS
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
  11. Section 2: Approaches and Methodologies
    1. Chapter 4: ASD-BI
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ASD-BI: AGILE KNOWLEDGE DISCOVERY METHODOLOGY
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    2. Chapter 5: A Proactive Approach for BI
      1. ABSTRACT
      2. INTRODUCTION
      3. BUSINESS INTELLIGENCE AS A DISCOVERY PROCESS
      4. REACTIVE APPROACH VS. PROACTIVE APPROACH
      5. A FRAMEWORK FOR A PROACTIVE BI
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    3. Chapter 6: Analyzing Customer Behavior Using Online Analytical Mining (OLAM)
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. APPROACHES
      5. PROPOSED SOLUTIONS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
  12. Section 3: Web and Text Mining Applications
    1. Chapter 7: Web Mining for the Integration of Data Mining with Business Intelligence in Web-Based Decision Support Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. OUR PROPOSAL
      5. DEFINING WEB DATA
      6. PRE-PROCESSING WEB DATA
      7. WEB DATA WAREHOUSING
      8. DEFINING PATTERN DISCOVERY AND ANALYSIS
      9. USING WEB MINING AS A PROCESS TO DEVELOP AN ADVANCED DECISION SUPPORT SYSTEM FOR AN E-NEWS WEB PORTAL
      10. EVALUATING OUR DECISION SUPPORT SYSTEM IN AN E-NEWS WEB PORTAL
      11. FUTURE RESEARCH DIRECTIONS
      12. CONCLUSION
      13. ACKNOWLEDGMENT
      14. REFERENCES
      15. ADDITIONAL READING
      16. KEY TERMS AND DEFINITIONS
      17. ENDNOTES
      18. APPENDIX
    2. Chapter 8: Text-Driven Reasoning and Multi-Structured Data Analytics for Business Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. MULTI-STRUCTURED DATA ANALYTICS: A BRIEF INTRODUCTION
      4. TEXT ANALYSIS DRIVEN BUSINESS INTELLIGENCE: A REVIEW
      5. APPLICATIONS OF TEXT MINING ON CUSTOMER COMMUNICATIONS: REVIEW OF RELATED WORK
      6. TEXT MINING TO OBTAIN OPEN SOURCE BUSINESS INTELLIGENCE: REVIEW OF RELATED WORK
      7. A NEW FRAMEWORK FOR INFORMATION FUSION AND INTEGRATION FOR BUSINESS INTELLIGENCE
      8. EVENT EXTRACTION AND CHARACTERIZATION FROM TEXT DOCUMENTS
      9. CHARACTERIZING BUSINESS CRITICAL EVENTS
      10. CORRELATION-BASED IMPACT ANALYSIS FOR MINED EVENTS ON STRUCTURED DATA
      11. CASE STUDY
      12. FUTURE RESEARCH DIRECTIONS
      13. CONCLUSION
      14. REFERENCES
      15. ADDITIONAL READING
      16. KEY TERMS AND DEFINITIONS
      17. ENDNOTES
  13. Section 4: Applications to Specific Domains
    1. Chapter 9: Pre-Triage Decision Support Improvement in Maternity Care by Means of Data Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND AND RELATED WORK
      4. IMPROVEMENT OF PRE-TRIAGE DECISION USING DATA MINING
      5. DISCUSSION
      6. CONCLUSION
      7. FUTURE RESEARCH DIRECTIONS
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
    2. Chapter 10: Business Intelligence and Nosocomial Infection Decision Making
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. CASE STUDY: NOSOCOMIAL INFECTION INCIDENCE INDICATORS IN CHP
      5. RESULTS AND DISCUSSION
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
    3. Chapter 11: A Comprehensive Workflow for Enhancing Business Bankruptcy Prediction
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. KDD FOR BUSINESS INTELLIGENCE
      5. KDD FOR BANKRUPTCY PREDICTION
      6. DISCUSSION AND FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
  14. Section 5: Software Issues
    1. Chapter 12: Open Source Software Integrations
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. INTEGRATING OPEN SOURCE SOFTWARE
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
      10. ENDNOTES
      11. APPENDIX
    2. Chapter 13: 3rd Order Analytics Demand Planning
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. OUR APPROACH TO THE INTEGRATION OF BI AND PREDICTIVE ANALYTICS
      5. THE CASE STUDY
      6. DEFINING OUR NOTION OF THE ORDERS OF ANALYTICS
      7. A CASE STUDY INTEGRATING 3 ORDER AND 2 ORDER ANALYTICS
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
      10. REFERENCES
      11. ADDITIONAL READING
      12. KEY TERMS AND DEFINITIONS
  15. Compilation of References
  16. About the Contributors