Anomaly Detection and Complex Event Processing over IoT Data Streams: With Application to Electrocardiogram (ECG) Patient Data Monitoring presents advanced processing techniques for IoT data streams. 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. Bio-metric signals, such as the massive amount of raw ECG signals from the sensors are processed dynamically across the data pipeline and classified with modern machine learning approaches based on the Hierarchical Temporal Memory (HTM) and Convolutional Neural Network (CNN) algorithms.
The book discusses adaptive solutions that can be extended to other use cases to enable a complex analysis of patient data in a historical, predictive and even prescriptive application scenario.
- Provides the state-of-the-art in IoT Data Stream Processing, Semantic Data Enrichment, Reasoning and Knowledge
- Covers extraction (Anomaly Detection)
- Illustrates new, scalable and reliable processing techniques based on IoT stream technologies
- Offers applications to new, real-time anomaly detection scenarios in the health domain
1. Related work and literature review 2. IoT Stream processing and Semantic Processing 3. IoT, Edge and Cloud Architecture 4. Case Study: Scalable IoT data processing and reasoning ecosystem in the field of Health 5. Communication protocols 6. Conceptual Design: Architecture 7. Technical design: Data Processing
ECG Data 8. Anomaly detection, classification and complex event processing 9. Computational Results and Analysis 10. Rule-based Software Systems for eHealth 11. Emerging Research Issues and Challenges 12. Conclusions
Patrick Schneider holds a BSc in Business Informatics from the DHBW Mannheim, Germany, and an MSc in Master in Informatics Research Innovation-Data Science from the Faculty of Informatics of Barcelona at the Universitat Politècnica de Catalunya. His areas of interest included -but are not limited- to Data Science with a focus on Real-World application of Machine Learning with specific emphasis in the field of IoT, Big Data architectures and process optimization. He participates regularly in Program Committees of International Conferences and will be in touch with Elsevier team to promote the book.
Fatos Xhafa received his PhD in Computer Science in 1998 from the Department of Computer Science of the Technical University of Catalonia (UPC), Barcelona, Spain. Currently, he holds a permanent position of Professor Titular at UPC, BarcelonaTech. He was a Visiting Professor at Birkbeck College, University of London (UK) during academic year 2009-2010 and Research Associate at Drexel University, Philadelphia (USA) during academic term 2004/2005. Dr. Xhafa has widely published in peer reviewed international journals, conferences/workshops, book chapters and edited books and proceedings in the field (http://dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos). He is awarded teaching and research merits by Spanish Ministry of Science and Education. Dr. Xhafa has an extensive editorial and reviewing service. He is editor in Chief of International Journal of Grid and Utility Computing and International Journal of Space-based and Situated Computing from Inderscience. He is actively participating in the organization of several international conferences and workshops. His research interests include parallel and distributed algorithms, optimization, networking, P2P and Cloud computing, security and trustworthy computing, among others. He can be reached at email@example.com and more information can be found at http://www.cs.upc.edu/~fatos/