Supporting Iterative Virtual Reality Analytics Design and Evaluation by Systematic Generation of Surrogate Clustered Datasets

Virtual Reality (VR) is a promising technology platform for immersive visual analytics. However, the design space of VR analytics interface design is vast and difficult to explore using traditional A/B comparisons in formal or informal controlled experiments— a fundamental part of an iterative design process. A key factor that complicates such comparisons is the dataset. Exposing participants to the same dataset in all conditions introduces an unavoidable learning effect. On the other hand, using different datasets for all experimental conditions introduces the dataset itself as an uncontrolled variable, which reduces internal validity to an unacceptable degree. In this paper, we propose to rectify this problem by introducing a generative process for synthesizing clustered datasets for VR analytics experiments. This process generates datasets that are distinct while simultaneously allowing systematic comparisons in experiments. A key advantage is that these datasets can then be used in iterative design processes. In a two-part experiment, we show the validity of the generative process and demonstrate how new insights in VR-based visual analytics can be gained using synthetic datasets.