Embodied provenance for immersive sensemaking

Yidan Zhang, Barrett Ens, Kadek Ananta Satriadi, Ying Yang, Sarah Goodwin

Research output: Contribution to journalArticleResearchpeer-review


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 the concept of embodied provenance, the use of three-dimensional space and embodied interactions in supporting recalling, reproducing, annotating and sharing analysis history in immersive environments. We design a conceptual framework for embodied provenance by highlighting a set of design criteria for analytic provenance drawn from prior work and identifying essential properties 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 virtual space. We present a use case scenario of energy consumption analysis and evaluated the system through a qualitative evaluation with 17 participants, which show the system's potential for assisting analytic provenance using embodiment. Our exploration of embodied provenance through this prototype provides lessons learnt to guide the design of immersive analytic tools for embodied provenance.

Original languageEnglish
Article number435
Pages (from-to)198–216
Number of pages19
JournalProceedings of the ACM on Human-Computer Interaction
Issue numberISS
Publication statusPublished - 1 Nov 2023


  • embodied provenance
  • immersive analytics
  • sensemaking
  • spatial memory

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