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Managing Big Data in Cloud Computing Environments

Book Description

Cloud computing has proven to be a successful paradigm of service-oriented computing, and has revolutionized the way computing infrastructures are abstracted and used. By means of cloud computing technology, massive data can be managed effectively and efficiently to support various aspects of problem solving and decision making. Managing Big Data in Cloud Computing Environments explores the latest advancements in the area of data management and analysis in the cloud. Providing timely, research-based information relating to data storage, sharing, extraction, and indexing in cloud systems, this publication is an ideal reference source for graduate students, IT specialists, researchers, and professionals working in the areas of data and knowledge engineering.

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. Preface
  7. Acknowledgment
  8. Section 1: Foundational Issues in Big Data Management in Cloud Computing Environments
    1. Chapter 1: Challenges and Opportunities in Big Data Processing
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. NOSQL DATABASES
      6. BIG DATA WITH NoSQL AND HADOOP
      7. TOWARDS NEW ARCHITECTURES FOR BIG DATA PROCESSING
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
      10. ACKNOWLEDGMENT
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    2. Chapter 2: A Cloud-Aware Distributed Object Storage System to Retrieve Large Data via HTML5-Enabled Web Browsers
      1. ABSTRACT
      2. INTRODUCTION
      3. HIGH PERFORMANCE STORAGE SYSTEMS
      4. CASE STUDY: A CLOUD-AWARE OBJECT STORAGE SYSTEM (CADOS)
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    3. Chapter 3: Security in Cloud of Things (CoT)
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. SECURITY REQUIREMENTS
      5. A SECURE SCHEME FOR CLOUD OF THINGS (CoT)
      6. CONCLUSION
      7. RECOMMENDATIONS
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    4. Chapter 4: CCCE
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORKS
      4. BACKGROUND
      5. CCCE DEVELOPMENT AND REQUIREMENTS
      6. CRYPTOGRAPHIC SERVICE LAYER
      7. CCCE MAIN BUILDING BLOCKS
      8. CCCE PHASES
      9. DISCUSSIONS
      10. CONCLUSION AND FUTURE WORKS
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    5. Chapter 5: Handling Critical Issues of Big Data on Cloud
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND WORK
      4. CHALLENGES IN HANDLING BIG DATA
      5. TYPES OF DATA
      6. METHODS TO HANDLE BIG DATA
      7. WHAT IS CLOUD?
      8. CLOUD COMPUTING CHARACTERISTICS
      9. ANALYSIS OF EXISTING CLOUD-BASED BIG DATA SOLUTIONS
      10. CASE 1: BIG DATA STORAGE ON CLOUD
      11. CASE 2: GOOGLE APP ENGINE
      12. CASE 3: CISCO’S S-CLOUD ARCHITECTURE
      13. CLOUD COMPUTING CHALLENGES
      14. CLOUD COMPUTING IMPLEMENTATIONS
      15. TECHNICAL CHALLENGES
      16. HANDLING CRITICAL ISSUES OF BIG DATA ON CLOUD
      17. FUTURE RESEARCH DIRECTIONS
      18. CONCLUSION
      19. REFERENCES
      20. KEY TERMS AND DEFINITIONS
  9. Section 2: Managing Big Data of Special Types in Cloud Computing Environments
    1. Chapter 6: Modeling and Indexing Spatiotemporal Trajectory Data in Non-Relational Databases
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. NON-RELATIONAL DATABASES
      5. MODELING TRAJECTORIES IN NON-RELATIONAL DATABASES
      6. TYPES OF QUERIES AND INDEXING TRAJECTORIES
      7. EXPERIMENTS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERM AND DEFINITIONS
    2. Chapter 7: Parallel Queries of Cluster-Based k Nearest Neighbor in MapReduce
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. PARTITIONED METHOD BASED ON VORONOI DIAGRAM
      5. THE DISTANCE BETWEEN POINTS OF VORONOI DIAGRAM
      6. PARALLEL k-MEANS ALGORITHM USING MAPREDUCE
      7. EXPERIMENTS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    3. Chapter 8: Knowledge as a Service Framework for Collaborative Data Management in Cloud Environments - Disaster Domain
      1. ABSTRACT
      2. INTRODUCTION
      3. MOTIVATING SCENARIO
      4. BACKGROUND
      5. DISASTER-CDM FRAMEWORK
      6. DATA MANAGEMENT FOR FILE-STYLE DATA
      7. CASE STUDY
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
      10. REFERENCES
      11. ADDITIONAL READING
      12. KEY TERMS AND DEFINITIONS
    4. Chapter 9: A Review of RDF Storage in NoSQL Databases
      1. ABSTRACT
      2. INTRODUCTION
      3. RDF MODEL AND RDF DATA STORAGE
      4. ACKNOWLEDGMENT
      5. REFERENCES
      6. ADDITIONAL READING
      7. KEY TERMS AND DEFINITIONS
      8. ENDNOTES
  10. Section 3: Two Application Scenarios of Big Data Management in Cloud Computing Environments
    1. Chapter 10: The Attitudes of Chinese Organizations Towards Cloud Computing
      1. ABSTRACT
      2. INTRODUCTION
      3. CLOUD COMPUTING
      4. ADVANTAGES AND DISADVANTAGES OF CLOUD COMPUTING
      5. CLOUD COMPUTING IN CHINESE ORGANIZATIONS
      6. RESEARCH METHOD AND QUESTION
      7. PARTICIPANTS
      8. RESULTS
      9. DISCUSSION
      10. LIMITATIONS
      11. CONCLUSION
      12. REFERENCES
      13. ADDITIONAL READING
      14. KEY TERMS AND DEFINITIONS
    2. Chapter 11: Green and Energy-Efficient Computing Architecture for E-Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. REVIEW OF EXISITNG WORKS
      5. COGALA ARCHITECTURE
      6. FUTURE RESEARCH DIRECTIONS
      7. REFERENCES
  11. Compilation of References
  12. About the Contributors