EDBT 2026 Demo / reviewers in the wild / expert
Guillaume Truong
dblp:385/8358
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0004-1516-9623ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 67% Virtual and augmented reality · 33% | |
| Human-computer interaction and pervasive computing
1 paper |
Usability and user experience research · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality
immersive visualization |
0.8 | 1 | 2024 | Memory Recall for Data Visualizations in Mixed Reality, Virtual Reality, 3D and 2D · IEEE Trans. Vis. Comput. Graph. 2024 |
Visualization and visual analytics › interactive visualization
mixed reality visualization |
0.8 | 1 | 2024 | Memory Recall for Data Visualizations in Mixed Reality, Virtual Reality, 3D and 2D · IEEE Trans. Vis. Comput. Graph. 2024 |
Visualization and visual analytics › perception
perception in visualization |
0.8 | 1 | 2024 | Memory Recall for Data Visualizations in Mixed Reality, Virtual Reality, 3D and 2D · IEEE Trans. Vis. Comput. Graph. 2024 |
Methods — techniques the papers use, named apart from their topics
user study · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Memory Recall for Data Visualizations in Mixed Reality, Virtual Reality, 3D and 2DabstractThis article explores how the ability to recall information in data visualizations depends on the presentation technology. Participants viewed 10 Isotype visualizations on a 2D screen, in 3D, in Virtual Reality (VR) and in Mixed Reality (MR). To provide a fair comparison between the three 3D conditions, we used LIDAR to capture the details of the physical rooms, and used this information to create our textured 3D models. For all environments, we measured the number of visualizations recalled and their order (2D) or spatial location (3D, VR, MR). We also measured the number of syntactic and semantic features recalled. Results of our study show increased recall and greater richness of data understanding in the MR condition. Not only did participants recall more visualizations and ordinal/spatial positions in MR, but they also remembered more details about graph axes and data mappings, and more information about the shape of the data. We discuss how differences in the spatial and kinesthetic cues provided in these different environments could contribute to these results, and reasons why we did not observe comparable performance in the 3D and VR conditions. Christophe Hurter, Bernice E. Rogowitz, Guillaume Truong, Tiffany Andry, Hugo Romat, Ludovic Gardy, Fereshteh Amini, Nathalie Henry Riche |
IEEE Trans. Vis. Comput. Graph. | 3 |