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Effective Big Data Management and Opportunities for Implementation

Book Description

“Big data” has become a commonly used term to describe large-scale and complex data sets which are difficult to manage and analyze using standard data management methodologies. With applications across sectors and fields of study, the implementation and possible uses of big data are limitless. Effective Big Data Management and Opportunities for Implementation explores emerging research on the ever-growing field of big data and facilitates further knowledge development on methods for handling and interpreting large data sets. Providing multi-disciplinary perspectives fueled by international research, this publication is designed for use by data analysts, IT professionals, researchers, and graduate-level students interested in learning about the latest trends and concepts in big data.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Preface
    1. ORGANIZATION OF THE BOOK
  6. Acknowledgment
  7. Chapter 1: Big Data
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND AND NEED FOR BIG DATA ANALYTICS
    4. BIG DATA: DEFINITION, DIMENSIONS, AND SOURCES
    5. BIG DATA VALUE CHAIN
    6. TOOLS FOR COLLECTING, PREPROCESSING AND ANALYZING BIG DATA
    7. SOFTWARE TOOLS FOR HANDLING BIG DATA
    8. APPLICATION OF BIG DATA ANALYTICS
    9. TECHNOLOGICAL GROWTH AND TECHNOLOGICAL LIMITATIONS
    10. RESEARCH AREAS WITH TECHNOLOGICAL ENHANCEMENTS
    11. CONCLUSION
    12. REFERENCES
    13. KEY TERMS AND DEFINITIONS
  8. Chapter 2: Introducing Data Structures for Big Data
    1. ABSTRACT
    2. INTRODUCTION
    3. PRELIMINARIES OF THE DATA STRUCTURE r-ATRAIN FOR BIG DATA
    4. FUNDAMENTAL OPERATIONS ON THE DATA STRUCTURE ‘r-ATRAIN’
    5. r-TRAIN STACK AND r-ATRAIN STACK
    6. INTRODUCING r-TRAIN STACK (TRAIN STACK)
    7. INTRODUCING r-ATRAIN STACK (ATRAIN STACK)
    8. r-TRAIN QUEUE AND r-ATRAIN QUEUE
    9. r-TRAIN BINARY TREE AND r-ATRAIN BINARY TREE
    10. EMPIRICAL ANALYSIS
    11. FUTURE RESEARCH DIRECTION
    12. CONCLUSION
    13. REFERENCES
    14. KEY TERMS AND DEFINITIONS
  9. Chapter 3: Big Data Mining
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. BIG DATA MINING ENVIRONMENT
    4. 3. BIG DATA MINING TOOLS
    5. 4. CHALLENGES IN BIG DATA MINING
    6. 5. APPLICATIONS OF BIG DATA MINING
    7. 6. CONCLUSION
    8. REFERENCES
  10. Chapter 4: An Empirical Study of NoSQL Databases for Big Data
    1. ABSTRACT
    2. INTRODUCTION
    3. BIG DATA MANAGEMENT, TECHNOLOGIES, AND APPLICATIONS
    4. MONGODB DATABASE DOWNLOAD AND INSTALLATION
    5. THE MONGODB SHELL INTERFACE
    6. CONNECTING JAVA TO MONGODB
    7. CONNECTING ASP.NET TO MONGODB
    8. SUMMARY
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  11. Chapter 5: The Challenges of Data Cleansing with Data Warehouses
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. DATA QUALITY
    4. 3. PROBLEMS
    5. 4. CONCLUSION
    6. REFERENCES
  12. Chapter 6: Big Data Analysis
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. BACKGROUND
    4. 3. PHASES IN THE BIG DATA LIFE CYCLE
    5. 4. CHALLENGES IN BIG DATA ANALYSIS
    6. CONCLUSION
    7. REFERENCES
  13. Chapter 7: Possibilities, Impediments, and Challenges for Network Security in Big Data
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. PRIOR RESEARCH WORKS ON NETWORK SECURITY FOR BIG DATA
    4. 3. POSSIBILITIES FOR NETWORK SECURITY IN BIG DATA
    5. 4. IMPEDIMENTS FOR NETWORK SECURITY IN BIG DATA
    6. 5. CHALLENGES FOR NETWORK SECURITY IN BIG DATA
    7. 6. EXISTING TOOLS AND TECHNIQUES BEST FOR BIG DATA SECURITY SOLUTIONS
    8. 7. CONCLUSION AND FUTURE DIRECTIONS
    9. REFERENCES
  14. Chapter 8: Mastering Big Data in the Digital Age
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. THEORY AND APPLICATIONS OF BIG DATA IN THE DIGITAL AGE
    5. FUTURE RESEARCH DIRECTIONS
    6. CONCLUSION
    7. REFERENCES
    8. ADDITIONAL READING
    9. KEY TERMS AND DEFINITIONS
  15. Chapter 9: Legal Responses to the Commodification of Personal Data in the Era of Big Data
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MAIN FOCUS OF THE ARTICLE
    5. SOLUTIONS AND RECOMMENDATIONS
    6. FUTURE CHALLENGES POSED BY WEARABLE TECH
    7. CONCLUSION
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
    10. ENDNOTE
  16. Chapter 10: Big Data and Business Decision Making
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. BACKGROUND
    4. 3. BIG DATA APPLICATIONS TO THE ECONOMY
    5. 4. THE BUSINESS OF BIG DATA
    6. 5. IMPLEMENTATION OF BIG DATA FOR BUSINESS ENVIRONMENT
    7. 6. TOOLS FOR IMPLEMENTATION OF BIG DATA
    8. 7. BIG DATA FOCUSED ON CLIENTS
    9. 8. APPLICATION EXAMPLES OF BIG DATA
    10. 9. CASES OF SUCCESSFUL IMPLEMENTATION OF BIG DATA
    11. 10. RECOMMENDATIONS
    12. 11. CONCLUSION
    13. REFERENCES
    14. ADDITIONAL READING
    15. KEY TERMS AND DEFINITIONS
  17. Chapter 11: Using Big Data to Improve the Educational Infrastructure and Learning Paradigm
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. EMERGING CHANGES IN EDUCATIONAL INFRASTRUCTURE AND LEARNING PARADIGM
    5. TECHNOLOGY – A SOURCE FOR GENERATING BIG DATA
    6. EXPLORING BIG DATA IN EDUCATION
    7. A MODEL FOR USING BIG DATA IN EDUCATION
    8. EDUCATIONAL DATA MINING METHODS
    9. APPLICATIONS AND LIMITATIONS
    10. FUTURE RESEARCH DIRECTIONS
    11. CONCLUSION
    12. REFERENCES
    13. ADDITIONAL READING
    14. KEY TERMS AND DEFINITIONS
  18. Chapter 12: Vehicle to Cloud
    1. ABSTRACT
    2. INTRODUCTION
    3. WIRELESS COMMUNICATIONS FOR VEHICULAR NETWORKS
    4. CLOUD COMPUTING AND BIG DATA
    5. APPLICATIONS IN V2C ECOSYSTEM WITH BIG DATA
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  19. Chapter 13: Point Cloud Manager
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND AND RELATED WORK
    4. MAIN FOCUS OF THE ARTICLE
    5. FUTURE RESEARCH DIRECTIONS
    6. CONCLUSION
    7. ACKNOWLEDGMENT
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  20. Chapter 14: Big Data Management in Financial Services
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MAIN FOCUS OF THE CHAPTER
    5. CONCLUSION
    6. REFERENCES
    7. ADDITIONAL READING
    8. KEY TERMS AND DEFINITIONS
  21. Chapter 15: Recommender System in the Context of Big Data
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. EXPERIMENTS AND EVALUATIONS
    5. DISCUSSION
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  22. Related References
  23. Compilation of References
  24. About the Contributors