Demo Data from VLDB 2026 / reviewers in the wild / expert
Christine H. Chih
dblp:90/5025
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2008
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 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 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
information visualization |
0.1 | 1 | 2008 | The persuasive phase of visualization · KDD 2008 |
Visualization and visual analytics › visual communication
persuasive visualization |
0.1 | 1 | 2008 | The persuasive phase of visualization · KDD 2008 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | The persuasive phase of visualizationabstractResearch in visualization often revolves around visualizing information. However, visualization is a process that extends over time from initial exploration to hypothesis confirmation, and even to result presentation. It is rare that the final phases of visualization are solely about information. In this paper we present a more biased kind of visualization, in which there is a message or set of assumptions behind the presentation that is of interest to both the presenter and the viewer, and emphasizes points that the presenter wants to convey to the viewer. This kind of persuasive visualization -- presenting data in a way that emphasizes a point or message -- is not only common in visualization, but also often expected by the viewer. Persuasive visualization is implicit in the deliberate emphasis on interestingness and also in the deliberate use of graphical elements that are processed preattentively by the human visual system, which automatically groups these elements and guiding attention so that they "stand out". We discuss how these ideas have been implemented in the Morpherspective system for automated generation of information graphics. Christine H. Chih, Douglas Stott Parker Jr. |
KDD | 1 |