The affordance of virtual reality to enable the sensory representation of multi‑dimensional data for immersive analytics: from experience to insight

Moloney, Jules, Spehar, Branka, Globa, Anastasia and Wang, Rui 2018, The affordance of virtual reality to enable the sensory representation of multi‑dimensional data for immersive analytics: from experience to insight, Journal of big data, vol. 5, pp. 1-19, doi: 10.1186/s40537-018-0158-z.

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Title The affordance of virtual reality to enable the sensory representation of multi‑dimensional data for immersive analytics: from experience to insight
Author(s) Moloney, JulesORCID iD for Moloney, Jules orcid.org/0000-0003-4173-3908
Spehar, Branka
Globa, AnastasiaORCID iD for Globa, Anastasia orcid.org/0000-0002-4749-5675
Wang, RuiORCID iD for Wang, Rui orcid.org/0000-0002-6600-6937
Journal name Journal of big data
Volume number 5
Article ID 53
Start page 1
End page 19
Total pages 19
Publisher SpringerOpen
Place of publication London, Eng.
Publication date 2018
ISSN 2196-1115
Keyword(s) Virtual reality
affordance
big data
multisensory representation
human interaction
Summary Using the theory of affordance from perceptual psychology and through discussion of literature within visual data mining and immersive analytics, a position for the multi-sensory representation of big data using virtual reality (VR) is developed. While it would seem counter intuitive, information-dense virtual environments are theoretically easier to process than simplified graphic encoding—if there is alignment with human ecological perception of natural environments. Potentially, VR affords insight into patterns and anomalies through dynamic experience of data representations within interactive, kinaesthetic audio-visual virtual environments. To this end we articulate principles that can inform the development of VR applications for immersive analytics: a mimetic approach to data mapping that aligns spatial, aural and kinaesthetic attributes with abstractions of natural environments; layered with constructed features that complement natural structures; the use of cross-modal sensory mapping; a focus on intermediate levels of contrast; and the adaptation of naturally occurring distribution patterns for the granularity and distribution of data. While it appears problematic to directly translate visual data mining techniques to VR, the ecological approach to human perception discussed in this article provides a new framework for big data visualization researchers to consider.
Language eng
DOI 10.1186/s40537-018-0158-z
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, The Author(s)
Persistent URL http://hdl.handle.net/10536/DRO/DU:30116438

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