Demo Data from EDBT 2026 / reviewers in the wild / expert
Tim van Gelder
dblp:23/5522 · also Timothy van Gelder
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0001-5606-495XORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1Applied, interdisciplinary, general and emerging 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.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Theoretical computer science
1 paper |
Computational geometry · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
hierarchical data visualization |
0.1 | 1 | 2011 | Hi-Trees and Their Layout · IEEE Trans. Vis. Comput. Graph. 2011 |
Computational geometry › graph drawing
tree drawing |
0.0 | 1 | 2011 | Hi-Trees and Their Layout · IEEE Trans. Vis. Comput. Graph. 2011 |
Methods — techniques the papers use, named apart from their topics
bottom-up tree layout algorithm · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Measuring Quality of General Reasoning
Alexandru Marcoci, Tim van Gelder, Simon Dennis 0001, Margaret Webb, Luke Rowe, Ashley Barnett, Tamar Primoratz, Ariel Kruger, Benjamin Stone, Michael L. Diamond, Morgan Saletta |
CogSci | 2 |
| 2011 | Hi-Trees and Their LayoutabstractWe introduce hi-trees, a new visual representation for hierarchical data in which, depending on the kind of parent node, the child relationship is represented using either containment or links. We give a drawing convention for hi-trees based on the standard layered drawing convention for rooted trees, then show how to extend standard bottom-up tree layout algorithms to draw hi-trees in this convention. We also explore a number of other more compact layout styles for layout of larger hi-trees and give algorithms for computing these. Finally, we describe two applications of hi-trees: argument mapping and business decision support. Kim Marriott, Peter Sbarski, Tim van Gelder, Daniel Prager, Andy Bulka |
IEEE Trans. Vis. Comput. Graph. | 3 |