Anomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring presents advanced processing techniques for IoT data streams and the anomaly detection algorithms over them. The book brings new advances and generalized techniques for processing IoT data streams, semantic data enrichment with contextual information at Edge, Fog and Cloud as well as complex event processing in IoT applications. The book comprises fundamental models, concepts and algorithms, architectures and technological solutions as well as their application to eHealth. Case studies, such as the bio-metric signals stream processing are presented –the massive amount of raw ECG signals from the sensors are processed dynamically across the data pipeline and classified with modern machine learning approaches including the Hierarchical Temporal Memory and Deep Learning algorithms.
The book discusses adaptive solutions to IoT stream processing that can be extended to different use cases from different fields of eHealth, to enable a complex analysis of patient data in a historical, predictive and even prescriptive application scenarios. The book ends with a discussion on ethics, emerging research trends, issues and challenges of IoT data stream processing.
1. Anomaly Detection and Complex Event Processing Over IoT Data Streams - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Dedication - p. Page iv 7. Contents - p. Page vi 8. List of figures - p. Page vi 9. List of tables - p. Page ix 10. Biographies - p. Page x 11. Preface - p. Page xii 12. List of Illustrations - p. Page xvii 13. List of Tables - p. Page xviii 14. Chapter 1 : IoT data streams: concepts and models: Introductory concepts and models - p. Page xviii 15. Chapter 2 : Data stream processing: models and methods: The complexity of data stream processing - p. Page 24 16. Chapter 3 : Anomaly detection: Concepts and methods - p. Page 46 17. Chapter 4 : Complex event processing: Building complex events from simple, atomic events - p. Page 63 18. Chapter 5 : Rule-based decision support systems for eHealth: Supporting actors and stakeholders of health systems - p. Page 83 19. Chapter 6 : Integrating technological solutions into innovative eHealth applications: At pace of advanced technology for eHealth - p. Page 97 20. Chapter 7 : IoT, edge, cloud architecture and communication protocols: Cloud digital ecosystem and protocols - p. Page 120 21. Chapter 8 : Machine learning: ML for eHealth systems - p. Page 145 22. Chapter 9 : Anomaly detection, classification and CEP with ML methods: Machine learning pipeline for medicine - p. Page 185 23. Chapter 10 : Architectures and technologies for stream processing: HPC stream processing platforms - p. Page 225 24. Chapter 11 : Technical design: data processing pipeline in eHealth: The case of ECG data sets - p. Page 256 25. Chapter 12 : Working procedure and analysis for an ECG dataset: Data stream processing in practice - p. Page 279 26. Chapter 13 : Ethics, emerging research trends, issues and challenges: Protecting patient data - p. Page 314 27. Index - p. Page 357
Anomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring presents advanced processing techniques for IoT data streams and the anomaly detection algorithms over them. The book brings new advances and generalized techniques for processing IoT data streams, semantic data enrichment with contextual information at Edge, Fog and Cloud as well as complex event processing in IoT applications. The book comprises fundamental models, concepts and algorithms, architectures and technological solutions as well as their application to eHealth. Case studies, such as the bio-metric signals stream processing are presented –the massive amount of raw ECG signals from the sensors are processed dynamically across the data pipeline and classified with modern machine learning approaches including the Hierarchical Temporal Memory and Deep Learning algorithms.
The book discusses adaptive solutions to IoT stream processing that can be extended to different use cases from different fields of eHealth, to enable a complex analysis of patient data in a historical, predictive and even prescriptive application scenarios. The book ends with a discussion on ethics, emerging research trends, issues and challenges of IoT data stream processing.
1. Anomaly Detection and Complex Event Processing Over IoT Data Streams - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Dedication - p. Page iv 7. Contents - p. Page vi 8. List of figures - p. Page vi 9. List of tables - p. Page ix 10. Biographies - p. Page x 11. Preface - p. Page xii 12. List of Illustrations - p. Page xvii 13. List of Tables - p. Page xviii 14. Chapter 1 : IoT data streams: concepts and models: Introductory concepts and models - p. Page xviii 15. Chapter 2 : Data stream processing: models and methods: The complexity of data stream processing - p. Page 24 16. Chapter 3 : Anomaly detection: Concepts and methods - p. Page 46 17. Chapter 4 : Complex event processing: Building complex events from simple, atomic events - p. Page 63 18. Chapter 5 : Rule-based decision support systems for eHealth: Supporting actors and stakeholders of health systems - p. Page 83 19. Chapter 6 : Integrating technological solutions into innovative eHealth applications: At pace of advanced technology for eHealth - p. Page 97 20. Chapter 7 : IoT, edge, cloud architecture and communication protocols: Cloud digital ecosystem and protocols - p. Page 120 21. Chapter 8 : Machine learning: ML for eHealth systems - p. Page 145 22. Chapter 9 : Anomaly detection, classification and CEP with ML methods: Machine learning pipeline for medicine - p. Page 185 23. Chapter 10 : Architectures and technologies for stream processing: HPC stream processing platforms - p. Page 225 24. Chapter 11 : Technical design: data processing pipeline in eHealth: The case of ECG data sets - p. Page 256 25. Chapter 12 : Working procedure and analysis for an ECG dataset: Data stream processing in practice - p. Page 279 26. Chapter 13 : Ethics, emerging research trends, issues and challenges: Protecting patient data - p. Page 314 27. Index - p. Page 357
