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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Request a sales quoteAnomaly 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.
- 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
Computer Scientists, Engineers, Medical Engineers and Health Professionals working in the data stream and e-Health fields. Research, development in: Data science for health, Data standardization, longitudinal data studies, Data-driven reasoning software systems in eHealth, Remote patient monitoring, Monitoring Elderly at home.
- 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
- No. of pages: 406
- Language: English
- Edition: 1
- Published: January 7, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780128238189
- eBook ISBN: 9780128238196
PS
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, SpainFX
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, SpainRead Anomaly Detection and Complex Event Processing Over IoT Data Streams on ScienceDirect