Defining Embodied Provenance for Immersive Sensemaking
Yidan Zhang, Barrett M. Ens, Kadek Ananta Satriadi, Ying Yang, Sarah Goodwin · 2023
Immersive analytics research has explored how embodied data representations and interactions can be used to engage users in sensemaking. Prior research has broadly overlooked the potential of immersive space for supporting analytic provenance, the understanding of sensemaking processes through users’ interaction histories. We propose a concept of embodied provenance, the use of three-dimensional space and embodiment in supporting recalling, reproducing, annotating and sharing analysis history in immersive environments. We highlight a set of design criteria for analytic provenance drawn from prior work and propose a conceptual framework for embodied provenance. We develop a prototype system in virtual reality to demonstrate the concept and support the conceptual framework by providing multiple data views and embodied interaction metaphors in a large space. We present a use case scenario of energy consumption analysis, which shows the system’s potential for assisting analytic provenance using embodiment.