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Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence

Book Description

Big data is a well-trafficked subject in recent IT discourse and does not lack for current research. In fact, there is such a surfeit of material related to big data—and so much of it of questionably reliability, thanks to the high-gloss efforts of savvy tech-marketing gurus—that it can, at times, be difficult for a serious academician to navigate. The Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence cuts through the haze of glitz and pomp surrounding big data and offers a simple, straightforward reference-source of practical academic utility. Covering such topics as cloud computing, parallel computing, natural language processing, and personalized medicine, this volume presents an overview of current research, insight into recent advances, and gaps in the literature indicative of opportunities for future inquiry and is targeted toward a broad, interdisciplinary audience of students, academics, researchers, and professionals in fields of IT, networking, and data-analytics.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
  6. Foreword
  7. Preface
    1. ORGANIZATION OF THE BOOK
    2. SECTION 1: PRESENTING BIG DATA AND ITS PERSPECTIVE
    3. SECTION 2: PRESENTING WEB INTELLIGENCE AND ITS PERSPECTIVE
    4. SECTION 3: EXPLORING THE CURRENT AND FUTURE TRENDS OF BIG DATA AND WEB INTELLIGENCE
  8. Acknowledgment
  9. Section 1: Presenting Big Data and Its Perspective
    1. Chapter 1: Importance of Big Data
      1. ABSTRACT
      2. I. INTRODUCTION
      3. II. BACKGROUND
      4. III. PROPERTIES OF BIG DATA
      5. IV. DATA MINING AND BIG DATA
      6. V. DATA MINING ANALYSIS
      7. VI. BIG DATA ANALYSIS
      8. VII. POTENTIALS OF BIG DATA IN FIVE DOMAINS
      9. VIII. IMPORTANCE OF BIG DATA
      10. IX. SERVICE GENERATED BIG DATA
      11. X. CHALLENGES AND ISSUES
      12. XI. FUTURE RESEARCH DIRECTIONS
      13. XII. CONCLUSION
      14. REFERENCES
      15. ADDITIONAL READING
      16. KEY TERMS AND DEFINITIONS
    2. Chapter 2: Security and Privacy Issues of Big Data
      1. ABSTRACT
      2. INTRODUCTION
      3. BIG DATA CHALLENGES TO INFORMATION SECURITY AND PRIVACY
      4. SOLUTIONS/PROPOSALS TO ADDRESS BIG DATA SECURITY AND PRIVACY CHALLENGES
      5. A CASE STUDY IN A SECURE SOCIAL APPLICATION
      6. A CASE STUDY FOR AN INTELLIGENT INTRUSION DETECTION/PREVENTION SYSTEM ON A SOFTWARE-DEFINED NETWORK
      7. BIG DATA SECURITY: FUTURE DIRECTIONS
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
      10. ENDNOTES
    3. Chapter 3: Big Data Security Management
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. BIG DATA SECURITY MANAGEMENT
      5. RESEARCH ISSUES AND DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    4. Chapter 4: Big Data in Telecommunications
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. NETWORK TRAFFIC STEERING WITH CROWD INTELLIGENCE
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
  10. Section 2: Presenting Web Intelligence and Its Perspective
    1. Chapter 5: Web Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. WEB INTELLIGENCE
      4. BIG DATA AND THE UNCERTAINTY
      5. FRAMEWORK OVERVIEW
      6. EVALUATION
      7. CONCLUSION
      8. REFERENCE
      9. KEY TERMS AND DEFINITIONS
      10. ENDNOTES
      11. APPENDIX: EVALUATION QUERIES
    2. Chapter 6: Intelligent Management and Efficient Operation of Big Data
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. FROM BIG DATA TO USABLE KNOWLEDGE
      4. 3. PERFORMANCE OF PROCESSING AND NETWORKING (CLOUD) INFRASTRUCTURES
      5. 4. A CASE STUDY FOR AN INTELLIGENT INTERNET EXCHANGE POINT
      6. 5. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    3. Chapter 7: Big Data Mining Based on Computational Intelligence and Fuzzy Clustering
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BIG DATA CHARACTERISTICS
      4. 3. DATA MINING CHALLENGES WITH BIG DATA
      5. 4. BACKGROUND AND RELATED WORK
      6. 5. CLUSTER ANALYSIS
      7. 6. CLUSTERING METHODS
      8. 7. METHODOLOGY
      9. 8. EVALUATION CRITERIA
      10. 9. EXPERIMENTAL RESULTS AND DISCUSSION
      11. 10. FUTURE RESEARCH DIRECTION
      12. 11. CONCLUSION
      13. REFERENCES
      14. KEY TERMS AND DEFINITIONS
    4. Chapter 8: Big Data and Web Intelligence for Condition Monitoring
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. BIG DATA IN STRUCTURAL HEALTH MONITORING
      5. PROPOSED METHODOLOGY
      6. BIG DATA AND CLOUD COMPUTING
      7. PROPOSED INFRASTRUCTURE
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
      10. ACKNOWLEDGMENT
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    5. Chapter 9: Prediction of Missing Associations from Information System Using Intelligent Techniques
      1. ABSTRACT
      2. INTRODUCTION
      3. INFORMATION SYSTEM
      4. FOUNDATIONS OF ROUGH SET
      5. ORDERED INFORMATION SYSTEM
      6. BAYESIAN CLASSIFICATION
      7. PROPOSED PREDICTION MODEL
      8. AN APPLICATION ON MARKETING STRATEGIES
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
      11. REFERENCES
    6. Chapter 10: Big Data and Web Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. DEFINING THE MP VIA LOGICAL DECISION TREE
      4. LDT TO BDD CONVERSION
      5. NEW APPROACH TO REDUCE THE COMPUTATIONAL COST
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
      10. APPENDIX
    7. Chapter 11: Web 2.0 Mash-Up System for Real Time Data Visualisation and Analysis Using OSS
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. AIMS AND OBJECTIVES
      4. 3. PROBLEM STATEMENT
      5. 4. RELATED WORK
      6. 5. INFORMATION VISUALISATION MODEL
      7. 6. PERFORMANCE ANALYSIS
      8. 7. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
  11. Section 3: Exploring the Current and Future Trends of Big Data and Web Intelligence
    1. Chapter 12: The Politics of Access to Information
      1. ABSTRACT
      2. INTRODUCTION
      3. HYPER CONNECTIVITY: GLOBALISATION, THE INTERNET, AND THE THREAT TO NATIONAL SOVEREIGNTY
      4. THE NETWORKING REVOLUTION: ICTs AND THE DIGITAL DIVIDE
      5. MIND THE GAP: EXPLORING THE CONSEQUENCES OF THE DIGITAL DIVIDE FOR DEVELOPMENT
      6. THE POLITICS OF ACCESS TO INFORMATION: OF DIGITAL INEQUALITIES AND DIGITAL DIVIDES
      7. FROM HUMAN FACTORS TO HUMAN ACTORS: THE DIGITAL DIVIDE AND PATTERNS OF HUMAN INTERACTION IN DEVELOPMENT
      8. BRIDGING THE DIGITAL DIVIDE: TOWARDS DEVELOPMENT OR DEPENDENCY?
      9. THE NETWORK SOCIETY: DIGITALLY EMPOWERED DEVELOPMENT
      10. EXPLORING THE DEVELOPMENT DIVIDE IN THE DIGITAL AGE
      11. CONCLUSION
      12. REFERENCES
      13. KEY TERMS AND DEFINITIONS
      14. ENDNOTES
    2. Chapter 13: Big Data and Data Modelling for Manufacturing Information Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. BIG DATA AND DATA MODELLING FOR MANUFACTURING INFORMATION SYSTEMS
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    3. Chapter 14: Modeling Big Data Analytics with a Real-Time Executable Specification Language
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. REALSPEC FOR BIG DATA ANALYTICS
      5. DISCUSSION AND FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    4. Chapter 15: Big Data Analysis in IoT
      1. ABSTRACT
      2. INTRODUCTION
      3. DATA WAREHOUSING AND MINING TECHNIQUES
      4. SOURCES OF BIG DATA
      5. DATA CHARACTERISTICS IN IoT
      6. NEED OF BIG DATA ANALYSIS AND AVAILABLE ANALYSIS TECHNIQUES
      7. CHALLENGES OF BIG DATA ANALYSIS IN IoT
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
      10. REFERENCES
      11. ADDITIONAL READING
      12. KEY TERMS AND DEFINITIONS
    5. Chapter 16: Navigation in Online Social Networks
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORK AND BACKGROUND
      4. 3. PREDICTION IN ONLINE SOCIAL NETWORK
      5. 4. PERFORMANCE EVALUATION
      6. 5. SIMULATION RESULTS
      7. 6. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    6. Chapter 17: Comparative Analysis of Efficient Platforms
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORK
      4. 3. ANALYSIS OF PLATFORMS AND COMPUTING TECHNOLOGIES
      5. 4. COMPARATIVE ANALYSIS OF PROGRAMMING LANGUAGES AND LIBRARIES
      6. 5. COMPARISON OF TECHNIQUES AND ALGORITHMS FOR DATA PARALLELISM
      7. 6. DISCUSSION
      8. REFERENCES
    7. Chapter 18: The Effectiveness of Big Data in Social Networks
      1. ABSTRACT
      2. PART I: SOCIAL NETWORKS ANALYSIS
      3. PART II: BIG DATA
      4. PART III: ALGORITHMS AROUND SOCIAL NETWORKS
      5. 4. CONCLUSION
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    8. Chapter 19: Analysis of VDM++ in Regression Test Suite
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. SECTION 1: FORMAL SPECIFICATION BASED TESTING
      5. TESTAF: A TEST AUTOMATION FRAMEWORK FOR CLASS TESTING USING OBJECT-ORIENTED FORMAL SPECIFICATIONS
      6. CONCLUSION
      7. REFERENCES
    9. Chapter 20: Web Usage Mining and the Challenge of Big Data
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE WEB USAGE MINING PROCESS (MAIN FOCUS)
      5. CHALLENGES AND RESEARCH ISSUES (FUTURE DIRECTIONS)
      6. FUTURE RESEARCH DIRECTION
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
  12. Compilation of References
  13. About the Contributors