Anomaly Detection and Complex Event Processing Over IoT Data Streams

With Application to eHealth and Patient Data Monitoring

1st Edition - January 7, 2022
Authors: Patrick Schneider, Fatos Xhafa
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 3 8 1 8 - 9
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 3 8 1 9 - 6

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… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Dedication
List of figures
List of tables
Biographies
Preface
Part 1: Fundamental concepts, models and methods
Chapter 1: IoT data streams: concepts and models
Abstract
1.1. IoT streams in the context of Big Data
1.2. Static vs. continuous data systems
1.3. Time variability in data streams
1.4. Dynamic data stream structure – the drift concept
1.5. IoT data streams in healthcare
1.6. Key features
References
Chapter 2: Data stream processing: models and methods
Abstract
2.1. Semantic primitives for stream processing
2.2. Window-based methods
2.3. Feature domain processing
2.4. Dimensionality reduction and analysis techniques
2.5. Key features
References
Chapter 3: Anomaly detection
Abstract
3.1. Introduction to anomaly detection
3.2. Challenges in anomaly detection
3.3. Anomaly types and detection techniques
3.4. Accuracy and prediction from anomaly detection
3.5. Key features
References
Chapter 4: Complex event processing
Abstract
4.1. Fundamental concept of CEP
4.2. Primitive functions for CEP
4.3. MultiIoT data stream in healthcare
4.4. CEP and AI
4.5. Key features
References
Chapter 5: Rule-based decision support systems for eHealth
Abstract
5.1. Introductory concepts and background in expert systems and decision support systems
5.2. Clinical Decision Support (CDS) basics
5.3. Implementation challenges of a DSS
5.4. Rule base systems in practice and their limitations
5.5. Key features
References
Part 2: Architectures and technological solutions for eHealth
Chapter 6: Integrating technological solutions into innovative eHealth applications
Abstract
6.1. Telemedicine network architectures
6.2. Telemedicine healthcare services
6.3. Telemedicine healthcare application
6.4. Ambient Assisted Living (AAL)
6.5. Telemedicine rehabilitation
6.6. Patient-physician interactions
6.7. Complex event processing for remote patient monitoring
6.8. Other related issues to patient monitoring
6.9. Key features
References
Chapter 7: IoT, edge, cloud architecture and communication protocols
Abstract
7.1. IoT architecture
7.2. Decentralized architecture paradigms
7.3. IoT architecture components
7.4. IoT stream processing infrastructures
7.5. Criteria for IoT architecture selection
7.6. Application protocols
7.7. Infrastructure protocols
7.8. Comparison of infrastructure protocols
7.9. Criteria for communication protocols selection
7.10. Key features
References
Chapter 8: Machine learning
Abstract
8.1. Learning models
8.2. Hybrid learning models
8.3. Statistical inference
8.4. Learning techniques
8.5. Federated learning
8.6. Handling concept drifts
8.7. ML frameworks
8.8. Key features
References
Chapter 9: Anomaly detection, classification and CEP with ML methods
Abstract
9.1. Anomaly detection by deep learning methods
9.2. Complex event processing
9.3. Key features
References
Part 3: Case study: scalable IoT data processing and reasoning
Chapter 10: Architectures and technologies for stream processing
Abstract
10.1. Introduction case study
10.2. Ingestion and communication system - Kafka
10.3. Communication protocol between producer devices and the Kafka ingestion system - MQTT
10.4. Stream processing and single-stream event detection - Faust
10.5. Complex event processing - Kafka Streams with KSQL
10.6. Other processing platform
10.7. Frameworks used in healthcare
10.8. Key features
References
Chapter 11: Technical design: data processing pipeline in eHealth
Abstract
11.1. Medical background of ECG data
11.2. ECG data sets
11.3. Dataset used in the case study
11.4. Pipeline: preprocessing module
11.5. Pipeline: core-processing module
11.6. Pipeline: anomaly detection, classification and complex event processing
11.7. Pipeline: classification and prediction
11.8. Key features
References
Chapter 12: Working procedure and analysis for an ECG dataset
Abstract
12.1. Processing and analysis of an ECG dataset by Faust cluster computing
12.2. Event processing network diagram
12.3. Data representation and enrichment
12.4. Key features
References
Chapter 13: Ethics, emerging research trends, issues and challenges
Abstract
13.1. Ethics and privacy in patient data monitoring
13.2. Noninvasive and personalized solutions for elderly based on IoT technologies
13.3. Detection vs prediction eHealth solutions at scale
13.4. Key features
References
Index

Patrick Schneider

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.

Affiliations and expertise

Affiliate Teaching Staff at Open University of Catalonia, Barcelona, Spain

Fatos Xhafa

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 [email protected]. Please visit also http://www.cs.upc.edu/~fatos/ and at http://dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos

Affiliations and expertise

Full Professor of Computer Science, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain

Life Sciences

Physical Sciences & Engineering

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