Breaking the Curse of Visual Analytics: Accommodating Virtual Reality in the Visualization Pipeline

Previous research has exposed the discrepancy between the subject of analysis (real world) and the actual data on which the analysis is performed (data world) as a critical weak spot in visual analysis pipelines. In this paper, we demonstrate how Virtual Reality (VR) can help to verify the correspondence of both worlds in the context of Information Visualization (InfoVis) and Visual Analytics (VA).

Immersion allows the analyst to dive into the data world and collate it to familiar real-world scenarios. If the data world lacks crucial dimensions, then these are also missing in created virtual environments, which may draw the analyst’s attention to inconsistencies between the database and the subject of analysis.

When situating VR in a generic visualization pipeline, we can confirm its basic equality compared to other mediums as well as possible benefits. To overcome the guarded stance of VR in InfoVis and VA, we present a structured analysis of arguments, exhibiting the circumstances that make VR a viable medium for visualizations.

As a further contribution, we discuss how VR can aid in minimizing the gap between the data world and the real world and present a use case that demonstrates two solution approaches. Finally, we report on initial expert feedback attesting the applicability of our approach in a real-world scenario for crime scene investigation.

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  author    = {Matthias Kraus and
               Matthias Miller and
               Juri Buchm{\"{u}}ller and
               Manuel Stein and
               Niklas Weiler and
               Daniel A. Keim and
               Mennatallah El{-}Assady},
  title     = {{Breaking the Curse of Visual Analytics: Accommodating Virtual Reality in the Visualization Pipeline}},
  booktitle = {Computer Vision, Imaging and Computer Graphics Theory and Applications},
  series    = {Communications in Computer and Information Science},
  volume    = {1182},
  pages     = {253--284},
  publisher = {Springer},
  year      = {2019},
  doi       = {10.1007/978-3-030-41590-7\_11}