Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Intelligent decision support for maintenance: an overview and future trends

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions

Load-shifting inside production lines

This thesis will have the goal to investigate possibilities for optimization of schedules of factory s production lines, to minimize the cost of electrical energy used. During this research, different operational constrains will have to be identified and taken into account, during optimization. At the end, based on discovered limitations, and other necessary assumptions, models for factory process will be devised in order to be optimized based on suitable algorithms

Industry 4.0 for SME

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsIndustry 4.0 has been growing within companies and impacting the economy and society, but this has been a more complex challenge for some types of companies. Due to the costs and complexity associated with Industry 4.0 technologies, small and medium enterprises face difficulties in adopting them. This thesis proposes to create a model that gives guidance and simplifies how to implement Industry 4.0 in SMEs with a low-cost perspective. It is intended that this model can be used as a blueprint to design and implement an Industry 4.0 project within a manufactory SME. To create the model, a literature review of the different fields regarding Industry 4.0 were conducted to understand the most suited technologies to leverage within the manufacturing industry and the different use cases where these would be applicable. After the model was built, expert interviews were conducted, and based on the received feedback, the model was tweaked, improved, and validated

Semantic Driven Approach for Rapid Application Development in Industrial Internet of Things

The evolution of IoT has revolutionized industrial automation. Industrial devices at every level such as field devices, control devices, enterprise level devices etc., are connected to the Internet, where they can be accessed easily. It has significantly changed the way applications are developed on the industrial automation systems. It led to the paradigm shift where novel IoT application development tools such as Node-RED can be used to develop complex industrial applications as IoT orchestrations. However, in the current state, these applications are bound strictly to devices from specific vendors and ecosystems. They cannot be re-used with devices from other vendors and platforms, since the applications are not semantically interoperable. For this purpose, it is desirable to use platform-independent, vendor-neutral application templates for common automation tasks. However, in the current state in Node-RED such reusable and interoperable application templates cannot be developed. The interoperability problem at the data level can be addressed in IoT, using Semantic Web (SW) technologies. However, for an industrial engineer or an IoT application developer, SW technologies are not very easy to use. In order to enable efficient use of SW technologies to create interoperable IoT applications, novel IoT tools are required. For this purpose, in this paper we propose a novel semantic extension to the widely used Node-RED tool by introducing semantic definitions such as iot.schema.org semantic models into Node-RED. The tool guides a non-expert in semantic technologies such as a device vendor, a machine builder to configure the semantics of a device consistently. Moreover, it also enables an engineer, IoT application developer to design and develop semantically interoperable IoT applications with minimal effort. Our approach accelerates the application development process by introducing novel semantic application templates called Recipes in Node-RED. Using Recipes, complex application development tasks such as skill matching between Recipes and existing things can be automated.We will present the approach to perform automated skill matching on the Cloud or on the Edge of an automation system. We performed quantitative and qualitative evaluation of our approach to test the feasibility and scalability of the approach in real world scenarios. The results of the evaluation are presented and discussed in the paper.Die Entwicklung des Internet der Dinge (IoT) hat die industrielle Automatisierung revolutioniert. Industrielle Geräte auf allen Ebenen wie Feldgeräte, Steuergeräte, Geräte auf Unternehmensebene usw. sind mit dem Internet verbunden, wodurch problemlos auf sie zugegriffen werden kann. Es hat die Art und Weise, wie Anwendungen auf industriellen Automatisierungssystemen entwickelt werden, deutlich verändert. Es führte zum Paradigmenwechsel, wo neuartige IoT Anwendungsentwicklungstools, wie Node-RED, verwendet werden können, um komplexe industrielle Anwendungen als IoT-Orchestrierungen zu entwickeln. Aktuell sind diese Anwendungen jedoch ausschließlich an Geräte bestimmter Anbieter und Ökosysteme gebunden. Sie können nicht mit Geräten anderer Anbieter und Plattformen verbunden werden, da die Anwendungen nicht semantisch interoperabel sind. Daher ist es wünschenswert, plattformunabhängige, herstellerneutrale Anwendungsvorlagen für allgemeine Automatisierungsaufgaben zu verwenden. Im aktuellen Status von Node-RED können solche wiederverwendbaren und interoperablen Anwendungsvorlagen jedoch nicht entwickelt werden. Diese Interoperabilitätsprobleme auf Datenebene können im IoT mithilfe von Semantic Web (SW) -Technologien behoben werden. Für Ingenieure oder Entwickler von IoT-Anwendungen sind SW-Technologien nicht sehr einfach zu verwenden. Zur Erstellung interoperabler IoT-Anwendungen sind daher neuartige IoT-Tools erforderlich. Zu diesem Zweck schlagen wir eine neuartige semantische Erweiterung des weit verbreiteten Node-RED-Tools vor, indem wir semantische Definitionen wie iot.schema.org in die semantischen Modelle von NODE-Red einführen. Das Tool leitet einen Gerätehersteller oder Maschinebauer, die keine Experten in semantische Technologien sind, an um die Semantik eines Geräts konsistent zu konfigurieren. Darüber hinaus ermöglicht es auch einem Ingenieur oder IoT-Anwendungsentwickler, semantische, interoperable IoT-Anwendungen mit minimalem Aufwand zu entwerfen und entwicklen Unser Ansatz beschleunigt die Anwendungsentwicklungsprozesse durch Einführung neuartiger semantischer Anwendungsvorlagen namens Rezepte für Node-RED. Durch die Verwendung von Rezepten können komplexe Anwendungsentwicklungsaufgaben wie das Abgleichen von Funktionen zwischen Rezepten und vorhandenen Strukturen automatisiert werden. Wir demonstrieren Skill-Matching in der Cloud oder am Industrial Edge eines Automatisierungssystems. Wir haben dafür quantitative und qualitative Bewertung unseres Ansatzes durchgeführt, um die Machbarkeit und Skalierbarkeit des Ansatzes in realen Szenarien zu testen. Die Ergebnisse der Bewertung werden in dieser Arbeit vorgestellt und diskutiert