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Managing and Processing Big Data in Cloud Computing

Book Description

Big data has presented a number of opportunities across industries. With these opportunities come a number of challenges associated with handling, analyzing, and storing large data sets. One solution to this challenge is cloud computing, which supports a massive storage and computation facility in order to accommodate big data processing. Managing and Processing Big Data in Cloud Computing explores the challenges of supporting big data processing and cloud-based platforms as a proposed solution. Emphasizing a number of crucial topics such as data analytics, wireless networks, mobile clouds, and machine learning, this publication meets the research needs of data analysts, IT professionals, researchers, graduate students, and educators in the areas of data science, computer programming, and IT development.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board
  6. Foreword
  7. Preface
  8. Acknowledgment
  9. Chapter 1: On the Dynamic Shifting of the MapReduce Timeout
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. GENERAL DESCRIPTION OF FAULT-TOLERANCE IN HADOOP
    5. THE TIMEOUT PROBLEM
    6. METHODOLOGY OVERVIEW
    7. EXPERIMENTAL ANALYSIS
    8. ONGOING WORK AND CHALLENGES
    9. RELATED WORK
    10. SUMMARY
    11. REFERENCES
  10. Chapter 2: Exploiting Semantics to Improve Classification of Text Corpus
    1. ABSTRACT
    2. INTRODUCTION
    3. BASIC APPROACHES OF WEB PAGE CLASSIFICATION
    4. WEB PAGE REPRESENTATION AND FEATURE EXTRACTION
    5. IMPORTANCE OF SEMANTICS IN CLASSIFICATION
    6. WORDNET
    7. SEMANTIC SIMILARITY BETWEEN WORDS
    8. SEMANTIC SIMILARITY BETWEEN TWO SYNSETS
    9. PROPOSED TECHNIQUE
    10. EXPERIMENTAL SETUP
    11. RESULTS AND DISCUSSION
    12. CONCLUSION
    13. REFERENCES
  11. Chapter 3: Centralized to Decentralized Social Networks
    1. ABSTRACT
    2. OSN INTRODUCTION AND MOTIVATION TOWARDS DOSN
    3. DOSN ARCHITECTURE
    4. DECENTRALIZATION
    5. CONCLUSION
    6. REFERENCES
  12. Chapter 4: Texture-Based Evolutionary Method for Cancer Classification in Histopathology
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORK
    4. PROPOSED FRAMEWORK
    5. EXPERIMENTS AND RESULTS
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. ACKNOWLEDGMENT
    9. REFERENCES
  13. Chapter 5: Ceaseless Virtual Appliance Streaming
    1. ABSTRACT
    2. BACKGROUND
    3. CONCLUSION
    4. REFERENCES
    5. KEY TERMS AND DEFINITIONS
  14. Chapter 6: Performance Evaluation of Routing Metrics in Wireless Multi-Hop Networks
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. BACKGROUND
    4. 3. ROUTING METRICS
    5. 4. IEEE 802.11n (MIMO)
    6. 5. MAIN FOCUS OF THE CHAPTER
    7. 6. PERFORMANCE EVALUATION
    8. 7. CONCLUSION
    9. REFERENCES
  15. Chapter 7: Mobile Cloud Computing Future Trends and Opportunities
    1. ABSTRACT
    2. INTRODUCTION
    3. MCC ARCHITECTURE
    4. BACKGROUND
    5. RESEARCH CHALLENGES AND ISSUES OF MOBILE CLOUD COMPUTING
    6. MOBILE CLOUD COMPUTING TRENDS
    7. COMPARISON OF DIFFERENT MOBILE CLOUD MODELS
    8. PROS AND CONS OF MOBILE CLOUD COMPUTING
    9. CONCLUSION
    10. REFERENCES
  16. Chapter 8: A Survey of Cloud-Based Services Leveraged by Big Data Applications
    1. ABSTRACT
    2. INTRODUCTION
    3. LITERATURE REVIEW
    4. FUTURE RESEARCH DIRECTIONS
    5. CONCLUSION
    6. REFERENCES
    7. KEY TERMS AND DEFINITIONS
  17. Chapter 9: Need of Hadoop and Map Reduce for Processing and Managing Big Data
    1. ABSTRACT
    2. INTRODUCTION
    3. LITERATURE SURVEY
    4. UNSTRUCTURED ANALYTICS
    5. DISCUSSION AND CHALLENGES
    6. CONCLUSION
    7. REFERENCES
  18. Chapter 10: Big Data Virtualization and Visualization on Cloud
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. CONCLUSION
    5. REFERENCE
  19. Chapter 11: Essentiality of Machine Learning Algorithms for Big Data Computation
    1. ABSTRACT
    2. INTRODUCTION
    3. LITERATURE SURVEY
    4. BIG DATA ANALYTICS
    5. COMPUTATIONAL FRAMEWORK
    6. APPLICATIONS OF MACHINE LEARNING IN DIFFERENT INDUSTRIES
    7. MACHINE LEARNING IN HADOOP
    8. A BIG DATA FRAMEWORK FOR CATEGORIZING TECHNICAL SUPPORT REQUESTS USING LARGE SCALE MACHINE LEARNING
    9. CONCLUSION
    10. REFERENCES
  20. Chapter 12: Big Data Virtualization and Visualization
    1. ABSTRACT
    2. INTRODUCTION
    3. SIGNIFICANCE OF BIG DATA AND APPLICATIONS
    4. BIG DATA VIRTUALIZATION AND VISUALIZATION
    5. SOLUTIONS AND RECOMMENDATIONS
    6. BIG DATA CHALLENGES IN DIVERSE DOMAINS
    7. FUTURE RESEARCH DIRECTIONS
    8. CONCLUSION
    9. REFERENCES
  21. Chapter 13: Resource Scheduling for Big Data on Cloud
    1. ABSTRACT
    2. INTRODUCTION
    3. TASK SCHEDULING TYPES
    4. TAXONOMY OF SCHEDULING ALGORITHMS
    5. SCHEDULING INDEPENDENT TASKS
    6. HEURISTIC MODEL FOR TASK EXECUTION SCHEDULING
    7. HEURISTIC MODELS FOR TASK SCHEDULING
    8. PROPOSED HEURISTIC ALGORITHM FOR TASK SCHEDULING
    9. EXPERIMENTAL RESULT
    10. REFERENCE
    11. ADDITIONAL READING
  22. Chapter 14: Green, Energy-Efficient Computing and Sustainability Issues in Cloud
    1. ABSTRACT
    2. INTRODUCTION
    3. GREEN SUSTAINABILITY ISSUES IN CLOUDS
    4. MONITORING AND METERING ENERGY EFFICIENCY
    5. REFERENCES
  23. Chapter 15: The Heterogeneity Paradigm in Big Data Architectures
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. CHALLENGES IN BIG DATA ARCHITECTURES
    5. HETEROGENEITY IN BIG DATA SYSTEMS
    6. SUMMARY AND FUTURE RESEARCH DIRECTIONS
    7. REFERENCES
  24. Related References
  25. Compilation of References
  26. About the Contributors