Demo Data from EDBT 2026 / reviewers in the wild / expert
Elizabeth G. Hetzler
dblp:76/1994 · also Beth Hetzler
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1
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 › text visualization
document collection visualization |
0.0 | 1 | 2002 | ThemeRiver: Visualizing Thematic Changes in Large Document Collections · IEEE Trans. Vis. Comput. Graph. 2002 |
Visualization and visual analytics
temporal data visualization |
0.0 | 1 | 2002 | ThemeRiver: Visualizing Thematic Changes in Large Document Collections · IEEE Trans. Vis. Comput. Graph. 2002 |
Visualization and visual analytics
visual encoding |
0.0 | 1 | 2002 | ThemeRiver: Visualizing Thematic Changes in Large Document Collections · IEEE Trans. Vis. Comput. Graph. 2002 |
Methods — techniques the papers use, named apart from their topics
timeline visualization · 0.0theme extraction · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | Meaningful Clusters
Antonio Sanfilippo, Gus Calapristi, Vernon L. Crow, Elizabeth G. Hetzler, Alan Turner |
LREC | 4 |
| 2002 | ThemeRiver: Visualizing Thematic Changes in Large Document CollectionsabstractThe ThemeRiver visualization depicts thematic variations over time within a large collection of documents. The thematic changes are shown in the context of a time-line and corresponding external events. The focus on temporal thematic change within a context framework allows a user to discern patterns that suggest relationships or trends. For example, the sudden change of thematic strength following an external event may indicate a causal relationship. Such patterns are not readily accessible in other visualizations of the data. We use a river metaphor to convey several key notions. The document collection's time-line, selected thematic content and thematic strength are indicated by the river's directed flow, composition and changing width, respectively. The directed flow from left to right is interpreted as movement through time and the horizontal distance between two points on the river defines a time interval. At any point in time, the vertical distance, or width, of the river indicates the collective strength of the selected themes. Colored "currents" flowing within the river represent individual themes. A current's vertical width narrows or broadens to indicate decreases or increases in the strength of the individual theme. Susan L. Havre, Elizabeth G. Hetzler, Paul Whitney, Lucy T. Nowell |
IEEE Trans. Vis. Comput. Graph. | 2 |