Anomaly Detection and Complex Event Processing over IoT Data Streams : With Application to eHealth and Patient Data Monitoring

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Anomaly Detection and Complex Event Processing over IoT Data Streams : With Application to eHealth and Patient Data Monitoring

Schneider, Patrick/ Xhafa, Fatos

ウェブストア価格 ¥26,540（本体¥24,128）
Academic Press Inc（2022/01発売）
外貨定価 US$ 130.00
ポイント 241pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 406 p.
言語 ENG
商品コード 9780128238189
DDC分類 004.678

Full Description

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.

Part I - Fundamental concepts, models and methods
1. IoT data streams: concepts and models
2. Data stream processing: models and methods
3. Anomaly detection
4. Complex event processing
5. Rule-based decision support systems for e-health

Part II - Architectures and technological solutions
6. State of the art in technological solutions for e-health
7. IoT, edge, cloud architecture and communication protocols
8. Machine learning
9. Anomaly detection, classification and complex event processing

Part III - Case study: scalable IoT data processing and reasoning ecosystem in the field of health
10. Conceptual design: architecture
11. Technical design: data processing
12. Working procedure and analysis for an ECG dataset
13. Ethics, emerging research trends, issues and challenges