EDBT 2026 Demo / reviewers in the wild / expert
Inyoup Na
dblp:249/2762
· 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 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 · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › visualization design
adaptive visualization |
0.9 | 1 | 2025 | Drillboards: Adaptive Visualization Dashboards for Dynamic Personalization of Visualization Experiences · IEEE Trans. Vis. Comput. Graph. 2025 |
Visualization and visual analytics › multi-view visualization
coordinated multiple views |
0.9 | 1 | 2025 | Drillboards: Adaptive Visualization Dashboards for Dynamic Personalization of Visualization Experiences · IEEE Trans. Vis. Comput. Graph. 2025 |
Visualization and visual analytics
visualization authoring |
0.3 | 1 | 2025 | Drillboards: Adaptive Visualization Dashboards for Dynamic Personalization of Visualization Experiences · IEEE Trans. Vis. Comput. Graph. 2025 |
Methods — techniques the papers use, named apart from their topics
formal chart representation vocabulary · 0.9chart merging rules · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Drillboards: Adaptive Visualization Dashboards for Dynamic Personalization of Visualization ExperiencesabstractWe present drillboards, a technique for adaptive visualization dashboards consisting of a hierarchy of coordinated charts that the user can drill down to reach a desired level of detail depending on their expertise, interest, and desired effort. This functionality allows different users to personalize the same dashboard to their specific needs and expertise. The technique is based on a formal vocabulary of chart representations and rules for merging multiple charts of different types and data into single composite representations. The drillboard hierarchy is created by iteratively applying these rules starting from a baseline dashboard, with each consecutive operation yielding a new dashboard with fewer charts and progressively more abstract and simplified views. We also present an authoring tool for building drillboards and show how it can be applied to an agricultural dataset with hundreds of expert users. Our evaluation asked three domain experts to author drillboards for their own datasets, which we then showed to casual end-users with favorable outcomes. Sungbok Shin, Inyoup Na, Niklas Elmqvist |
IEEE Trans. Vis. Comput. Graph. | 2 |