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

by Patrick Schneider, Fatos Xhafa PhD

Overview

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.

Product Details

ISBN-13:	9780128238189
Publisher:	Elsevier Science
Publication date:	01/19/2022
Pages:	406
Product dimensions:	7.50(w) x 9.25(h) x (d)

About the Author

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 Technical University of Catalonia (UPC). He is affiliate teaching staff at Open University of Catalonia (UOC). His areas of interest include - but are not limited to - Data Science, focusing on Real-World application of Machine Learning with specific emphasis in IoT, Big Data architectures, Process Optimization and Process Mining. He regularly participates in Program Committees of International Conferences.

Fatos Xhafa, PhD in Computer Science, is Full Professor at the Technical University of Catalonia (UPC), Barcelona, Spain. He has held various tenured and visiting professorship positions. He was a Visiting Professor at the University of Surrey, UK (2019/2020), Visiting Professor at the Birkbeck College, University of London, UK (2009/2010) and a Research Associate at Drexel University, Philadelphia, USA (2004/2005). He was a Distinguished Guest Professor at Hubei University of Technology, China, for the duration of three years (2016-2019). Prof. Xhafa has widely published in peer reviewed international journals, conferences/workshops, book chapters, edited books and proceedings in the field (H-index 55). He has been awarded teaching and research merits by the Spanish Ministry of Science and Education, by IEEE conferences and best paper awards. Prof. Xhafa has an extensive editorial service. He is founder and Editor-In-Chief of Internet of Things - Journal - Elsevier (Scopus and Clarivate WoS Science Citation Index) and of International Journal of Grid and Utility Computing, (Emerging Sources Citation Index), and AE/EB Member of several indexed Int'l Journals. Prof. Xhafa is a member of IEEE Communications Society, IEEE Systems, Man & Cybernetics Society and Founder Member of Emerging Technical Subcommittee of Internet of Things.
His research interests include IoT and Cloud-to-thing continuum computing, massive data processing and collective intelligence, optimization, security and trustworthy computing and machine learning, among others. He can be reached at [e-mail]. Please visit also http://www.cs.upc.edu/~fatos/ and at http://dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos

Table of Contents

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

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From the Publisher

Presents novel approaches to semantic data enrichment, complex event processing and reasoning over IoT data streams