Speaker: DI Alexander Gall
Abstract
Traditional desktop-based analysis faces significant limitations in handling complex material data. This presentation explores how immersive analytics can enhance analytical capabilities by leveraging users' spatial cognition and natural interaction.
I will showcase our implementation of immersive workspaces across the reality-virtuality continuum, from fully virtual environments to augmented reality applications. Special attention will be given to MARV, our novel AR visual analytics system that integrates primary and secondary XCT data visualization. MARV features three innovative techniques—MDD Glyphs with Skewness Kurtosis Mapper, Temporal Evolution Tracker, and Chrono Bins—designed to facilitate exploration of multidimensional distributions across multiple time steps.
Drawing from qualitative evaluations with materials experts, I will demonstrate how immersive workspaces improve analytical capabilities, reduce cognitive load, and facilitate new insights impossible with conventional methods. The presentation concludes with emerging visualization concepts for immersive analysis, including 4D visualizations of battery data and novel transition techniques that bridge the gap between traditional and immersive analytical approaches, opening new pathways for multidimensional data exploration across scientific domains.
Bio
DI Alexander Gall is currently working as Research Associate at the University of Passau, Germany in the Professorship for Cognitive Sensor Systems (Faculty of Computer Science and Mathematics). He worked several years at the Computed Tomography Research Group at the University of Applied Sciences Upper Austria, Wels Campus. He received his BSc degree in Medical Informatics from TU Wien for the comparison of vessel segmentation techniques. He received his MSc from TU Wien in Visual Computing for immersive analytics of multidimensional volumetric data from composite materials. Alexander Gall is currently focusing on his PhD thesis, which includes immersive visualizations of multi-dimensional industrial X-ray computed tomography (XCT) data from non-destructive testing (NDT) and cross-reality/virtuality analytics.
