Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people’s behaviors, and how to unobtrusively provide better services.
It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality.
In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation.
Indexing: The books of this series are submitted to EI-Compendex and SCOPUS
1. Cover image - p. cover 2. Title page - p. cover 3. Table of Contents - p. cover 4. Copyright - p. iv 5. List of Contributors - p. iv 6. Preface - p. xv 7. Part I: Big Data Architecture and Platforms - p. xx 8. Chapter 1: Big Data: A Classification of Acquisition and Generation Methods - p. xx 9. Chapter 2: Cloud Computing Infrastructure for Data Intensive Applications - p. 20 10. Chapter 3: Open Source Private Cloud Platforms for Big Data - p. 62 11. Part II: Big Data Processing and Management - p. 80 12. Chapter 4: Efficient Nonlinear Regression-Based Compression of Big Sensing Data on Cloud - p. 80 13. Chapter 5: Big Data Management on Wireless Sensor Networks - p. 98 14. Chapter 6: Extreme Learning Machine and Its Applications in Big Data Processing - p. 116 15. Part III: Big Data Analytics and Services - p. 150 16. Chapter 7: Spatial Big Data Analytics for Cellular Communication Systems - p. 150 17. Chapter 8: Cognitive Applications and Their Supporting Architecture for Smart Cities - p. 166 18. Chapter 9: Deep Learning for Human Activity Recognition - p. 184 19. Chapter 10: Neonatal Cry Analysis and Categorization System Via Directed Acyclic Graph Support Vector Machine - p. 204 20. Part IV: Big Data Intelligence and IoT Systems - p. 222 21. Chapter 11: Smart Building Applications and Information System Hardware Co-Design - p. 222 22. Chapter 12: Smart Sensor Networks for Building Safety - p. 240 23. Chapter 13: The Internet of Things and Its Applications - p. 255 24. Chapter 14: Smart Railway Based on the Internet of Things - p. 279 25. Index - p. 297
Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people’s behaviors, and how to unobtrusively provide better services.
It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality.
In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation.
Indexing: The books of this series are submitted to EI-Compendex and SCOPUS
1. Cover image - p. cover 2. Title page - p. cover 3. Table of Contents - p. cover 4. Copyright - p. iv 5. List of Contributors - p. iv 6. Preface - p. xv 7. Part I: Big Data Architecture and Platforms - p. xx 8. Chapter 1: Big Data: A Classification of Acquisition and Generation Methods - p. xx 9. Chapter 2: Cloud Computing Infrastructure for Data Intensive Applications - p. 20 10. Chapter 3: Open Source Private Cloud Platforms for Big Data - p. 62 11. Part II: Big Data Processing and Management - p. 80 12. Chapter 4: Efficient Nonlinear Regression-Based Compression of Big Sensing Data on Cloud - p. 80 13. Chapter 5: Big Data Management on Wireless Sensor Networks - p. 98 14. Chapter 6: Extreme Learning Machine and Its Applications in Big Data Processing - p. 116 15. Part III: Big Data Analytics and Services - p. 150 16. Chapter 7: Spatial Big Data Analytics for Cellular Communication Systems - p. 150 17. Chapter 8: Cognitive Applications and Their Supporting Architecture for Smart Cities - p. 166 18. Chapter 9: Deep Learning for Human Activity Recognition - p. 184 19. Chapter 10: Neonatal Cry Analysis and Categorization System Via Directed Acyclic Graph Support Vector Machine - p. 204 20. Part IV: Big Data Intelligence and IoT Systems - p. 222 21. Chapter 11: Smart Building Applications and Information System Hardware Co-Design - p. 222 22. Chapter 12: Smart Sensor Networks for Building Safety - p. 240 23. Chapter 13: The Internet of Things and Its Applications - p. 255 24. Chapter 14: Smart Railway Based on the Internet of Things - p. 279 25. Index - p. 297
