Anomaly Detection and Complex Event Processing Over Iot Data Streams: With Application to Ehealth and Patient Data Monitoring

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.

《物聯網數據流的異常檢測和複雜事件處理：應用於電子健康和患者數據監測》介紹了物聯網數據流的高級處理技術和相應的異常檢測算法。本書提出了處理物聯網數據流的新進展和通用技術，包括邊緣、霧和雲端環境下的語義數據豐富化以及物聯網應用中的複雜事件處理。本書包括基本模型、概念和算法、架構和技術解決方案，以及它們在電子健康領域的應用。書中還介紹了案例研究，例如生物測量信號流的處理-從傳感器中動態處理大量原始心電圖信號，並使用現代機器學習方法（包括階層時間記憶和深度學習算法）進行分類。

本書討論了適應性的物聯網數據流處理解決方案，可擴展到電子健康領域的不同用例，實現對患者數據的歷史、預測甚至指導性應用場景的複雜分析。書末還討論了物聯網數據流處理的倫理、新興研究趨勢、問題和挑戰。