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Multi-modal Data Exploration in a Mixed Reality Environment Using Coordinated Multiple Views

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12765)

Abstract

Immersive analytics is an emerging field of data exploration and analysis in immersive environments. The main idea is to use visual analytics in a fully immersive 3D space. The availability of immersive Extended Reality systems has increased tremendously recently, but it is still not as widely used as conventional 2D displays. We describe an immersive analysis system for spatio-temporal data and compare how it performs in an immersive environment and on a conventional 2D display. We provide a novel view called map-plot that enables analysis and exploration of spatial time series data. We also design an embodied interaction for the map-plot view. The approach is realized based on the coordinated multiple views paradigm. In addition to the map-plot view, several standard views are available. Voice commands and spatial audio are used to support interaction and increase users’ embodiment. The findings from a user study show that developed system is much more efficient in a real immersive environment than using conventional 2D displays.

Keywords

  • Immersive analytics
  • Mixed reality
  • Embodied interaction
  • Coordinated multiple views

D. Sardana and S. Y. Kahu—The authors contributed equally to the paper.

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Acknowledgements

VRVis is funded by BMK, BMDW, Styria, SFG, Tyrol and Vienna Business Agency in the scope of COMET - Competence Centers for Excellent Technologies (879730) which is managed by FFG.

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Correspondence to Disha Sardana .

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Sardana, D., Kahu, S.Y., Gračanin, D., Matković, K. (2021). Multi-modal Data Exploration in a Mixed Reality Environment Using Coordinated Multiple Views. In: Yamamoto, S., Mori, H. (eds) Human Interface and the Management of Information. Information Presentation and Visualization. HCII 2021. Lecture Notes in Computer Science(), vol 12765. Springer, Cham. https://doi.org/10.1007/978-3-030-78321-1_26

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  • DOI: https://doi.org/10.1007/978-3-030-78321-1_26

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