VLDB 2026 Research / reviewers in the wild / expert
Julia Eunju Nam
dblp:46/9529
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author
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 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
high-dimensional data exploration |
0.2 | 1 | 2013 | TripAdvisorN-D: A Tourism-Inspired High-Dimensional Space Exploration Framework with Overview and Detail · IEEE Trans. Vis. Comput. Graph. 2013 |
Visualization and visual analytics
clustering |
0.0 | 1 | 2013 | TripAdvisorN-D: A Tourism-Inspired High-Dimensional Space Exploration Framework with Overview and Detail · IEEE Trans. Vis. Comput. Graph. 2013 |
Methods — techniques the papers use, named apart from their topics
motion parallax · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | TripAdvisorN-D: A Tourism-Inspired High-Dimensional Space Exploration Framework with Overview and DetailabstractGaining a true appreciation of high-dimensional space remains difficult since all of the existing high-dimensional space exploration techniques serialize the space travel in some way. This is not so foreign to us since we, when traveling, also experience the world in a serial fashion. But we typically have access to a map to help with positioning, orientation, navigation, and trip planning. Here, we propose a multivariate data exploration tool that compares high-dimensional space navigation with a sightseeing trip. It decomposes this activity into five major tasks: 1) Identify the sights: use a map to identify the sights of interest and their location; 2) Plan the trip: connect the sights of interest along a specifyable path; 3) Go on the trip: travel along the route; 4) Hop off the bus: experience the location, look around, zoom into detail; and 5) Orient and localize: regain bearings in the map. We describe intuitive and interactive tools for all of these tasks, both global navigation within the map and local exploration of the data distributions. For the latter, we describe a polygonal touchpad interface which enables users to smoothly tilt the projection plane in high-dimensional space to produce multivariate scatterplots that best convey the data relationships under investigation. Motion parallax and illustrative motion trails aid in the perception of these transient patterns. We describe the use of our system within two applications: 1) the exploratory discovery of data configurations that best fit a personal preference in the presence of tradeoffs and 2) interactive cluster analysis via cluster sculpting in N-D. Julia Eunju Nam, Klaus Mueller 0001 |
IEEE Trans. Vis. Comput. Graph. | 1 |
| 2009 | A high-dimensional feature clustering approach to support knowledge-assisted visualization
Julia Eunju Nam, Mauricio Maurer, Klaus Mueller 0001 |
Comput. Graph. | 1 |