Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications.
To help realize Big Data’s full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. List of contributors - p. xv 6. About the Editors - p. xix 7. Preface - p. xxi 8. Acknowledgments - p. xxv 9. Part I: Big Data Science - p. xxv 10. Chapter 1: BDA = ML + CC - p. xxv 11. Chapter 2: Real-Time Analytics - p. 38 12. Chapter 3: Big Data Analytics for Social Media - p. 61 13. Chapter 4: Deep Learning and Its Parallelization - p. 94 14. Chapter 5: Characterization and Traversal of Large Real-World Networks - p. 118 15. Part II: Big Data Infrastructures and Platforms - p. 136 16. Chapter 6: Database Techniques for Big Data - p. 136 17. Chapter 7: Resource Management in Big Data Processing Systems - p. 159 18. Chapter 8: Local Resource Consumption Shaping: A Case for MapReduce - p. 188 19. Chapter 9: System Optimization for Big Data Processing - p. 214 20. Chapter 10: Packing Algorithms for Big Data Replay on Multicore - p. 238 21. Part III: Big Data Security and Privacy - p. 266 22. Chapter 11: Spatial Privacy Challenges in Social Networks - p. 266 23. Chapter 12: Security and Privacy in Big Data - p. 283 24. Chapter 13: Location Inferring in Internet of Things and Big Data - p. 308 25. Part IV: Big Data Applications - p. 335 26. Chapter 14: A Framework for Mining Thai Public Opinions - p. 335 27. Chapter 15: A Case Study in Big Data Analytics: Exploring Twitter Sentiment Analysis and the Weather - p. 355 28. Chapter 16: Dynamic Uncertainty-Based Analytics for Caching Performance Improvements in Mobile Broadband Wireless Networks - p. 388 29. Chapter 17: Big Data Analytics on a Smart Grid: Mining PMU Data for Event and Anomaly Detection - p. 415 30. Chapter 18: eScience and Big Data Workflows in Clouds: A Taxonomy and Survey - p. 429 31. Index - p. 455
Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications.
To help realize Big Data’s full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. List of contributors - p. xv 6. About the Editors - p. xix 7. Preface - p. xxi 8. Acknowledgments - p. xxv 9. Part I: Big Data Science - p. xxv 10. Chapter 1: BDA = ML + CC - p. xxv 11. Chapter 2: Real-Time Analytics - p. 38 12. Chapter 3: Big Data Analytics for Social Media - p. 61 13. Chapter 4: Deep Learning and Its Parallelization - p. 94 14. Chapter 5: Characterization and Traversal of Large Real-World Networks - p. 118 15. Part II: Big Data Infrastructures and Platforms - p. 136 16. Chapter 6: Database Techniques for Big Data - p. 136 17. Chapter 7: Resource Management in Big Data Processing Systems - p. 159 18. Chapter 8: Local Resource Consumption Shaping: A Case for MapReduce - p. 188 19. Chapter 9: System Optimization for Big Data Processing - p. 214 20. Chapter 10: Packing Algorithms for Big Data Replay on Multicore - p. 238 21. Part III: Big Data Security and Privacy - p. 266 22. Chapter 11: Spatial Privacy Challenges in Social Networks - p. 266 23. Chapter 12: Security and Privacy in Big Data - p. 283 24. Chapter 13: Location Inferring in Internet of Things and Big Data - p. 308 25. Part IV: Big Data Applications - p. 335 26. Chapter 14: A Framework for Mining Thai Public Opinions - p. 335 27. Chapter 15: A Case Study in Big Data Analytics: Exploring Twitter Sentiment Analysis and the Weather - p. 355 28. Chapter 16: Dynamic Uncertainty-Based Analytics for Caching Performance Improvements in Mobile Broadband Wireless Networks - p. 388 29. Chapter 17: Big Data Analytics on a Smart Grid: Mining PMU Data for Event and Anomaly Detection - p. 415 30. Chapter 18: eScience and Big Data Workflows in Clouds: A Taxonomy and Survey - p. 429 31. Index - p. 455
