Presenting E2COMATION Results at the BTW 2025
I’m glad to have had the opportunity to present our paper at BTW 2025 last week. In the paper, we could outline one of our main contributions in the finishing European research project E2COMATION, how we could enable plant workers to perform their own (visual) analytics on shopfloor data for smart manufacturing. With my colleagues Harald, Mohamed, and Kanishk, it was a special experience to participate in the conference.
The BTW conference is the most important database conference in the German-speaking area. Every two years since 1985, it has served as a central forum for the exchange of information between scientists, practitioners, and users on topics of database and information system technology. Focus topics are data integration and extraction, provenance management, data protection, scalable big data analytics, hardware technologies on a small scale up to cloud computing as well as new application areas and architectural approaches for the support of machine learning in the context of artificial intelligence.
More information about our paper, titled “Enabling Smart Manufacturing with Visual Analytics for Plant Workers”[1]
Abstract: Smart manufacturing is increasingly making use of visual analytics to optimize production or to identify early problem signs. However, current solutions and approaches require professionals, especially from the data science area, to make use of it, which is not affordable for most production companies. In this paper, we describe first a best practice to sensorize plants from the wood and beverage industry to enable smart manufacturing in general. Second, we describe a new approach that aims at providing easy-to-use visual analytics functionalities that are designed to be used directly by plant workers. Plant workers usually have encompassing experience in the production and the plant, but lack of computer experience and corresponding mathematical knowledge for data analysis. Through lowering the barriers for plant workers in performing data analysis of the IoT sensors with simplified and almost automated analysis functions would give them the ability to gain insights into the production and achieve similar production optimizations and problem preventions as data science experts could. The main contributions of this article are on the one hand the best practice of how production lines of the wood and beverage industry could be made ready for smart manufacturing, but also an approach that enables non-data scientists, especially plant workers, to perform sufficient analysis about optimal production settings and early problem cause identification.
References
- (2025): Enabling Smart Manufacturing with Visual Analytics for Plant Workers. In: Klettke, Meike; Schenkel, Ralf; Henrich, Andreas; Nicklas, Daniela; Schüle, Maximilian E.; Meyer-Wegener, Klaus (Ed.): Datenbanksysteme für Business, Technologie und Web (BTW 2025), pp. 665-678, Gesellschaft für Informatik, Bonn, 2025, ISSN: 2944-7682.
