In many application areas we now see leaps to the next level in terms of scale of heterogeneous data, e.g. with technologies and techniques such as single cell profiling, spatial transcriptomics, large-scale science maps, ubiquitous sensing, and high-resolution imagery. This creates challenges regarding the cognitive and perceptual limits of a human analyst, but also creates an opportunity to exploit the affordances of mixed reality immersive environments (IE) in order to improve analysis efficiency and quality as well as the analyst's experience.
This project aims at deepening the understanding of scale and complexity effects for data analysis in IE. To this end, the scale and complexity space for immersive analytics will be characterised, and effects and limits of scale and complexity dimensions will be quantified. In a combination of methodological work and user studies, we will investigate the use of abstraction and aggregation methods and the potential and impact of navigation concepts for scalability.