{"product_id":"anomaly-detection-and-complex-event-processing-over-iot-data-streams-with-application-to-ehealth-and-patient-data-monitoring-9780128238189","title":"Anomaly Detection and Complex Event Processing Over IoT Data Streams: With Application to eHealth and Patient Data Monitoring","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eAnomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring discusses advanced processing techniques for IoT data streams and anomaly detection algorithms, including semantic data enrichment, edge, fog, and cloud processing, and complex event processing in IoT applications. It presents case studies and adaptive solutions for eHealth, enabling complex analysis of patient data in historical, predictive, and prescriptive scenarios. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 406 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 19 January 2022\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Elsevier Science Publishing Co Inc\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eAnomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring is a comprehensive guide that delves into advanced processing techniques for IoT data streams and the anomaly detection algorithms applied to them. This book offers groundbreaking advancements and generalized methods for handling IoT data streams, including semantic data enrichment with contextual information at Edge, Fog, and Cloud, as well as complex event processing in IoT applications. It encompasses fundamental models, concepts, and algorithms, architectural solutions, and technological advancements, all of which are applied to the field of eHealth. Through case studies, such as the processing of bio-metric signals streams, the book showcases the dynamic processing of vast raw ECG signals from sensors using modern machine learning approaches, including Hierarchical Temporal Memory and Deep Learning algorithms. Additionally, it explores adaptive solutions to IoT stream processing that can be extended to diverse use cases across different domains of eHealth, enabling comprehensive analysis of patient data in historical, predictive, and prescriptive application scenarios. The book concludes with a thoughtful discussion on ethics, emerging research trends, issues, and challenges associated with IoT data stream processing, providing valuable insights for researchers and practitioners in this field.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 828g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 190 x 236 x 27 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9780128238189\u003c\/p\u003e","brand":"PatrickSchneider,Fatos, Barcelona, Spain) Xhafa","offers":[{"title":"Paperback \/ softback","offer_id":44096333218042,"sku":"9780128238189","price":94.1,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/1646252423630_book.jpg?v=1646933490","url":"https:\/\/shulphink.com\/products\/anomaly-detection-and-complex-event-processing-over-iot-data-streams-with-application-to-ehealth-and-patient-data-monitoring-9780128238189","provider":"Shulph Ink","version":"1.0","type":"link"}