{"id":1720,"date":"2025-06-26T13:47:45","date_gmt":"2025-06-26T13:47:45","guid":{"rendered":"https:\/\/iste-group.com\/%f0%9f%a7%ad-big-data-analytics-for-cyber-physical-systems-machine-learning-for-the-internet-of-things\/"},"modified":"2025-07-14T09:37:10","modified_gmt":"2025-07-14T09:37:10","slug":"%f0%9f%a7%ad-big-data-analytics-for-cyber-physical-systems-machine-learning-for-the-internet-of-things","status":"publish","type":"post","link":"https:\/\/iste-group.com\/en\/%f0%9f%a7%ad-big-data-analytics-for-cyber-physical-systems-machine-learning-for-the-internet-of-things\/","title":{"rendered":"\ud83e\udded Big Data Analytics for Cyber-Physical Systems: Machine Learning for the Internet of Things"},"content":{"rendered":"\n<p class=\"has-custom-color-3-color has-text-color has-link-color wp-elements-29e866ca17c7253ae4f9e4ac20c30e54\" style=\"padding-top:var(--wp--preset--spacing--10);padding-bottom:var(--wp--preset--spacing--10);font-size:clamp(0.875rem, 0.875rem + ((1vw - 0.2rem) * 0.625), 1.25rem);\"><strong><strong>\u2018From sensor data to smart decisions &#8211; big data analytics as the engine of networked systems\u2019<\/strong><\/strong><\/p>\n\n\n\n<p class=\"has-link-color wp-elements-95bf60095a70f0243e909c169c1e8720\" style=\"padding-top:var(--wp--preset--spacing--10);padding-bottom:var(--wp--preset--spacing--10);font-size:clamp(0.875rem, 0.875rem + ((1vw - 0.2rem) * 0.625), 1.25rem);\"><strong>Publication date:<\/strong> June 2019<\/p>\n\n\n\n<p class=\"has-link-color wp-elements-5c40d95bc72b3bbc47a9d0c4ebfba2a8\" style=\"padding-top:var(--wp--preset--spacing--10);padding-bottom:var(--wp--preset--spacing--10);font-size:clamp(0.875rem, 0.875rem + ((1vw - 0.2rem) * 0.625), 1.25rem);\">This book shows how large, heterogeneous data streams from cyber-physical systems (CPS) and the Internet of Things (IoT) can be transformed into reliable decisions using modern machine learning methods. It provides a practical introduction to sensor signal processing, IoT gateways, optimisation and decision making, highlights use cases from mobility, smart cities and Industry 4.0 and covers everything from mathematical principles to edge implementations. Readers are provided with best practices, \u2018winning stories\u2019 and real-life case studies that trace the path from data acquisition to value creation. The book thus acts as a bridge between research, technology and application and supports companies in scaling data-driven CPS sustainably.   <\/p>\n\n\n\n<p class=\"has-link-color wp-elements-8a0b2430aebfa36a1729fa0d3a1de02d\" style=\"padding-top:var(--wp--preset--spacing--10);padding-bottom:var(--wp--preset--spacing--10);font-size:clamp(0.875rem, 0.875rem + ((1vw - 0.2rem) * 0.625), 1.25rem);\"><strong>Publisher (Editors):<\/strong> <strong>Guido Dartmann<\/strong> (Trier University of Applied Sciences), <strong>Houbing Song<\/strong> (University of Maryland, Baltimore County), <strong>Anke Schmeink<\/strong> (RWTH Aachen University) \u2013 mit Beitr\u00e4gen von \u00fcber 40 internationalen Expert:innen aus Wissenschaft und Industrie<\/p>\n\n\n\n<div style=\"height:8px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-custom-color-3-color has-text-color has-link-color wp-elements-104c3b4736873db2d0e0cdfde62352ea\" style=\"font-size:clamp(0.875rem, 0.875rem + ((1vw - 0.2rem) * 0.625), 1.25rem);\"><a href=\"https:\/\/books.google.de\/books\/about\/Big_Data_Analytics_for_Cyber_Physical_Sy.html?id=pr2iDwAAQBAJ&amp;redir_esc=y\" target=\"_blank\" rel=\"noreferrer noopener\">\ud83d\udc49 <strong>\u201eBridges the gap between IoT, CPS, and mathematical modelling.\u201c<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u2018From sensor data to smart decisions &#8211; big data analytics as the engine of networked systems\u2019 Publication date: June 2019 This book shows how large, heterogeneous data streams from cyber-physical systems (CPS) and the Internet of Things (IoT) can be transformed into reliable decisions using modern machine learning methods. It provides a practical introduction to [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":1656,"comment_status":"open","ping_status":"open","sticky":false,"template":"publication","format":"standard","meta":{"footnotes":""},"categories":[169],"tags":[125,129,130,141,189,190],"class_list":["post-1720","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publication","tag-big-data-en","tag-cyber-physical-systems","tag-data-analytics-en-2","tag-edge-computing-en","tag-internet-of-things","tag-machine-learning"],"_links":{"self":[{"href":"https:\/\/iste-group.com\/en\/wp-json\/wp\/v2\/posts\/1720","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/iste-group.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/iste-group.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/iste-group.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/iste-group.com\/en\/wp-json\/wp\/v2\/comments?post=1720"}],"version-history":[{"count":1,"href":"https:\/\/iste-group.com\/en\/wp-json\/wp\/v2\/posts\/1720\/revisions"}],"predecessor-version":[{"id":1917,"href":"https:\/\/iste-group.com\/en\/wp-json\/wp\/v2\/posts\/1720\/revisions\/1917"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/iste-group.com\/en\/wp-json\/wp\/v2\/media\/1656"}],"wp:attachment":[{"href":"https:\/\/iste-group.com\/en\/wp-json\/wp\/v2\/media?parent=1720"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/iste-group.com\/en\/wp-json\/wp\/v2\/categories?post=1720"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/iste-group.com\/en\/wp-json\/wp\/v2\/tags?post=1720"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}