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Projeto de investigação
Big Data Value Spaces for COmpetitiveness of European COnnected Smart FacTories 4.0
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Publicações
A Framework for Big Data Sovereignty
Publication . Mertens, Christoph; Alonso, Jesús; Lázaro, Oscar; Palansuriya, Charaka; Böge, Gernot; Nizamis, Alexandros; Rousopoulou, Vaia; Ioannidis, Dimosthenis; Tzovaras, Dimitrios; Touma, Rizkallah; Tarzán, Miquel; Mallada, Diego; Figueiras, Paulo; Costa, Ruben; Graça, Diogo; Garcia, Gisela; Laibarra, Begoña; Celaya, Aitor; Sobonski, Piotr; Naeem, Azzam; Mozo, Alberto; Vakaruk, Stanislav; Sierra-García, J. Enrique; Pastor, Antonio; Rodríguez, Juan; Hildebrand, Marlène; Luniewski, Tomasz; Zietak, Wojciech; Lange, Christoph; Sipsas, Konstantinos; Poulakidas, Athanasios; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
The path that the European Commission foresees to leverage data in the best possible way for the sake of European citizens and the digital single market clearly addresses the need for a European Data Space. This data space must follow the rules, derived from European values. The European Data Strategy rests on four pillars: (1) Governance framework for access and use; (2) Investments in Europe’s data capabilities and infrastructures; (3) Competences and skills of individuals and SMEs; (4) Common European Data Spaces in nine strategic areas such as industrial manufacturing, mobility, health, and energy. The project BOOST 4.0 developed a prototype for the industrial manufacturing sector, called European Industrial Data Space (EIDS), an endeavour of 53 companies. The publication will show the developed architectural pattern as well as the developed components and introduce the required infrastructure that was developed for the EIDS. Additionally, the population of such a data space with Big Data enabled services and platforms is described and will be enriched with the perspective of the pilots that have been build based on EIDS.
Big Data-Driven Industry 4.0 Service Engineering Large-Scale Trials
Publication . Lázaro, Oscar; Alonso, Jesús; Figueiras, Paulo; Costa, Ruben; Graça, Diogo; Garcia, Gisela; Canepa, Alessandro; Calefato, Caterina; Vallini, Marco; Fournier, Fabiana; Hazout, Nathan; Skarbovsky, Inna; Poulakidas, Athanasios; Sipsas, Konstantinos; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
In the last few years, the potential impact of big data on the manufacturing industry has received enormous attention. This chapter details two large-scale trials that have been implemented in the context of the lighthouse project Boost 4.0. The chapter introduces the Boost 4.0 Reference Model, which adapts the more generic BDVA big data reference architectures to the needs of Industry 4.0. The Boost 4.0 reference model includes a reference architecture for the design and implementation of advanced big data pipelines and the digital factory service development reference architecture. The engineering and management of business network track and trace processes in high-end textile supply are explored with a focus on the assurance of Preferential Certification of Origin (PCO). Finally, the main findings from these two large-scale piloting activities in the area of service engineering are discussed.
A DIN Spec 91345 RAMI 4.0 Compliant Data Pipelining Model: An Approach to Support Data Understanding and Data Acquisition in Smart Manufacturing Environments
Publication . Nagorny, Kevin; Scholze, Sebastian; Colombo, Armando Walter; Oliveira, José Barata; DEE2010-C2 Robótica e Manufactura Integrada por Computador; DEE - Departamento de Engenharia Electrotécnica e de Computadores; Institute of Electrical and Electronics Engineers (IEEE)
Today, data scientists in the manufacturing domain are confronted with various communication standards, protocols and technologies to save and transfer various kinds of data. These circumstances makes it hard to understand, find, access and extract data needed for use case depended applications. One solution could be a data pipelining approach enforced by a semantic model which describes smart manufacturing assets itself and the access to their data along their life-cycle. Many research contributions in smart manufacturing already came out with with reference architectures like the RAMI 4.0 or standards for meta data description or asset classification. Our research builds upon these outcomes and introduces a semantic model based DIN Spec 91345 (RAMI 4.0) compliant data pipelining approach with the smart manufacturing domain as exemplary use case. This paper has a focus on the developed semantic model used to enable an easy data exploration, finding, access and extraction of data, compatible with various used communication standards, protocols and technologies used to save and transfer data.
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Entidade financiadora
European Commission
Programa de financiamento
H2020
Número da atribuição
780732