Xiyao Mei

dblp:383/5893 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › temporal data visualization
historical visualization
0.912025
ZuantuSet: A Collection of Historical Chinese Visualizations and Illustrations · CHI 2025
Visualization and visual analytics
visualization dataset
0.912025
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
YearPublicationVenuePosition
2025 ZuantuSet: A Collection of Historical Chinese Visualizations and Illustrations
abstract
Historical 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
CHI1