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Developments in Data Extraction, Management, and Analysis

Book Description

With the improvements of artificial intelligence, processor speeds and database sizes, the rapidly expanding field of data mining continues to provide advancing methods for managing databases and gaining knowledge. Developments in Data Extraction, Management, and Analysis is an essential collection of research on the area of data mining and analytics. Presenting the most recent perspectives on data mining subjects and current issues, this book is useful for practitioners and academics alike.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Associate Editors
    2. List of Reviewers
  5. Preface
    1. INTRODUCTION
    2. BIG DATA: AN OVERVIEW
    3. CURRENT APPROACHES IN BIG DATA MANAGEMENT
    4. CONCLUSION AND FUTURE WORK
  6. Chapter 1: Visual Mobility Analysis using T-Warehouse
    1. ABSTRACT
    2. INTRODUCTION
    3. SYSTEM ARCHITECTURE
    4. OLAP AND VISUALISATION
    5. APPLYING T-WAREHOUSE TO TRAFFIC DATA
    6. RELATED WORK
    7. CONCLUSION
  7. Chapter 2: A Query Language for Mobility Data Mining
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORK
    4. THE TWO-WORLDS MODEL
    5. THE TWO-WORLDS OPERATORS
    6. THE DATA MINING QUERY LANGUAGE
    7. THE GEOPKDD SYSTEM
    8. CONCLUSION
  8. Chapter 3: Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and its Application to (Vertically) Partitioned Spatio-Temporal Data
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORK
    4. PRELIMINARIES
    5. OUR ALGORITHM
    6. PRIVACY DISCUSSION
    7. COST ANALYSIS
    8. EXPERIMENTS
    9. CONCLUSION
  9. Chapter 4: Query Recommendations for OLAP Discovery-Driven Analysis
    1. ABSTRACT
    2. INTRODUCTION
    3. MOTIVATING EXAMPLE
    4. RELATED WORK
    5. OLAP DATA MODEL AND QUERY MODEL
    6. THE RECOMMENDER SYSTEM FRAMEWORK
    7. ALGORITHMS FOR THE RECOMMENDER SYSTEM
    8. IMPLEMENTING THE FRAMEWORK
    9. EXPERIMENTS
    10. ON THE FEASABILITY OF THE APPROACH
    11. CONCLUSION
  10. Chapter 5: Data Warehouse Testing
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORKS
    4. METHODOLOGICAL FRAMEWORK
    5. TESTING ACTIVITIES
    6. TEST COVERAGE
    7. A MODULAR TIMELINE FOR TESTING
    8. PRACTICAL EVIDENCES
    9. CONCLUSION
  11. Chapter 6: An Empirical Evaluation of Similarity Coefficients for Binary Valued Data
    1. ABSTRACT
    2. INTRODUCTION
    3. MOTIVATING EXAMPLES
    4. BACKGROUND ON SIMILARITY COEFFICIENTS
    5. METHODOLOGY
    6. EXPERIMENTAL RESULTS
    7. CONCLUSION
    8. APPENDIX A
    9. APPENDIX B
  12. Chapter 7: Dynamic View Management System for Query Prediction to View Materialization
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORKS
    4. JUSTIFICATION OF THE TECHNIQUE USED IN THE PROPOSED SYSTEM
    5. PROPOSED SYSTEM OVERVIEW AND ARCHITECTURE
    6. ALGORITHM OF PR_Q_PREDICTOR SYSTEM
    7. EXPERIMENTS
    8. CONCLUSION
  13. Chapter 8: Finding Associations in Composite Data Sets
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND AND RELATED WORK
    4. PROBLEM DEFINITION
    5. THE FUZZY APRIORI-T (CFARM) ALGORITHM
    6. AN EXAMPLE APPLICATION
    7. EXPERIMENTAL EVALUATION
    8. CONCLUSION
  14. Chapter 9: Automatic Item Weight Generation for Pattern Mining and its Application
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORK
    4. THE WEIGHTED ASSOCIATION RULE MINING (WARM) PROBLEM
    5. AUTOMATED WEIGHTING MECHANISM USING VALENCY MODEL
    6. RULE EVALUATION USING PRINCIPAL COMPONENTS ANALYSIS
    7. REAL WORLD APPLICATION: TEXT MINING USING THE VALENCY MODEL
    8. CONCLUSION
  15. Chapter 10: Weak Ratio Rules
    1. ABSTRACT
    2. INTRODUCTION
    3. PROBLEM STATEMENT
    4. PROPERTIES OF WRR
    5. MINING WRR
    6. UNCERTAINTY REASONING METHOD BASED ON WRR
    7. APPLICATION OF WRR
    8. CONCLUSION
  16. Chapter 11: Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model
    1. ABSTRACT
    2. INTRODUCTION
    3. REGULARIZED MULTIPLE CRITERIA LINEAR PROGRAMMING (RMCLP) CLASSIFICATION MODEL
    4. RULE EXTRACTION METHOD FOR RMCLP
    5. EXPERIMENTS
    6. CONCLUSION
  17. Chapter 12: Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORK
    4. BACKGROUND AND NOTATION
    5. DISCOVERING FREQUENT SUBSEQUENCES
    6. EXPERIMENTAL RESULTS
    7. CONCLUSION
  18. Chapter 13: HYBRIDJOIN for Near-Real-Time Data Warehousing
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORK
    4. PRELIMINARIES: MESHJOIN
    5. HYBRIDJOIN
    6. TESTS WITH LOCALITY OF DISK ACCESS
    7. EXPERIMENTS
    8. CONCLUSION AND FUTURE WORK
    9. APPENDIX
  19. Chapter 14: Data Field for Hierarchical Clustering
    1. ABSTRACT
    2. INTRODUCTION
    3. DATA FIELDS
    4. CLUSTERING ALGORITHMS WITH DATA FIELDS
    5. CASE STUDY
    6. CONCLUSION
  20. Chapter 15: Preserving Privacy in Time Series Data Mining
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORK
    4. TIME SERIES DATA PUBLISHING AND MINING SYSTEM
    5. METHODS FOR PRESERVING PRIVACY
    6. PERFORMANCE EVALUATION
    7. DISCUSSION
    8. CONCLUSION
  21. Compilation of References
  22. About the Contributors