EDBT 2026 Demo / reviewers in the wild / expert
Xiyao Mei
dblp:383/5893
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0002-3127-1519ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 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% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 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 › temporal data visualization
historical visualization |
0.9 | 1 | 2025 | ZuantuSet: A Collection of Historical Chinese Visualizations and Illustrations · CHI 2025 |
Visualization and visual analytics
visualization dataset |
0.9 | 1 | 2025 | ZuantuSet: A Collection of Historical Chinese Visualizations and Illustrations · CHI 2025 |
Methods — techniques the papers use, named apart from their topics
visual pattern analysis · 1.7semi-automatic collection pipeline · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | ZuantuSet: A Collection of Historical Chinese Visualizations and IllustrationsabstractHistorical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to consider contributions from different cultural frameworks to gain a comprehensive understanding. While there is extensive research on historical visualizations within the European cultural framework, this work shifts the focus to ancient China, a cultural context that remains underexplored by visualization researchers. To this aim, we propose a semi-automatic pipeline to collect, extract, and label historical Chinese visualizations. Through the pipeline, we curate ZuantuSet, a dataset with over 71K visualizations and 108K illustrations. We analyze distinctive design patterns of historical Chinese visualizations and their potential causes within the context of Chinese history and culture. We illustrate potential usage scenarios for this dataset, summarize the unique challenges and solutions associated with collecting historical Chinese visualizations, and outline future research directions. Xiyao Mei, Yu Zhang 0043, Chaofan Yang, Xiaoru Yuan |
CHI | 1 |