We are proud to share our latest collaborative research contribution to the field of visual analytics:
📄 Our paper “Cluster-Based Approach for Visual Anomaly Detection in Multivariate Welding Process Data Supported by User Guidance” has been presented by Josef Suschnigg at ACM IUI 2025 and is now officially published in the ACM Digital Library (Open Access):
🔗 https://dl.acm.org/doi/10.1145/3708359.3712076
🧠 The work introduces a human-centered visual analytics tool that supports unsupervised anomaly detection and clustering in multivariate time series data from autonomous welding processes — developed in close collaboration with our industry partner Fronius International.
👨🏭 By integrating domain expert knowledge into the analysis loop and guiding users to relevant patterns, the system enables effective process monitoring and decision support in industrial settings.
👏 Huge thanks to our team and partners for contributing to this impactful project!
