Demo Data from VLDB 2026 / reviewers in the wild / expert
Jennifer Roth
dblp:420/7724
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 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.
| Human-computer interaction and pervasive computing
1 paper |
Immersive interaction · 44% Interaction techniques and input · 44% User interface design and tools · 13% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input
target selection |
0.9 | 1 | 2025 | SliVR: A 360° VR-Hub for Fast Selections in Multiple Virtual Environments · ISMAR 2025 |
Methods — techniques the papers use, named apart from their topics
user study · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | SliVR: A 360° VR-Hub for Fast Selections in Multiple Virtual EnvironmentsabstractCurrent virtual reality (VR) systems limit users to a single virtual 3D environment (VE) at a time, which restricts their ability to engage with multiple VEs simultaneously. We present SLIVR, a VR-Hub designed to enhance multitasking capabilities by arranging multiple VEs in a regular polygon formation around the user. Each VE is visually represented by life-sized 2D previews that facilitate hub-based interactions. Direct Selection enables users to select objects within a VE directly from the preview, allowing them to remain within the hub. Additionally, Focus-mode enlarges a chosen 2D preview, providing access to objects beyond the initial viewport. In a user study ($\mathrm{N}=18$), we evaluated SLIVR against a conventional grid-based VR app launcher and a hub that utilized SLIVR's visualization but only supported Teleportation interactions. Our findings revealed that SLIVR improved target selection performance by 16.8% overall and by 30.5% when engaging with six VEs, compared to the grid-based app launcher. Notably, hub-based interactions accounted for 94.9% of selections, with Focus-mode being utilized six times more frequently than Teleportation. Kevin Linne, Sven Thomas 0002, Jennifer Roth, Martin Weigel 0001 |
ISMAR | 3 |