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Keyi Yang

dblp:333/9567 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 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%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › multi-view visualization
coordinated multiple views
0.912025
ChronoDeck: A Visual Analytics Approach for Hierarchical Time Series Analysis · IEEE Trans. Vis. Comput. Graph. 2025
Visualization and visual analytics › multi-view visualization
small multiples
0.912025
ChronoDeck: A Visual Analytics Approach for Hierarchical Time Series Analysis · IEEE Trans. Vis. Comput. Graph. 2025
Visualization and visual analytics
time series visualization
0.912025
ChronoDeck: A Visual Analytics Approach for Hierarchical Time Series Analysis · IEEE Trans. Vis. Comput. Graph. 2025
Visualization and visual analytics
dimensionality reduction
0.312025
ChronoDeck: A Visual Analytics Approach for Hierarchical Time Series Analysis · IEEE Trans. Vis. Comput. Graph. 2025

Methods — techniques the papers use, named apart from their topics

taxonomy · 0.9expert interviews · 0.9case study · 0.9
YearPublicationVenuePosition
2025 Bayesian deep multi-instance learning for student performance prediction based on campus big data
Jiayang Huang, Keyi Yang, Quan Wang 0006, Pengfei Yang 0001, Ziling Ruan, Zhiqiang Zhang 0001
Neurocomputing2
2025 ChronoDeck: A Visual Analytics Approach for Hierarchical Time Series Analysis
abstract
Hierarchical time series data comprises a collection of time series aggregated at multiple levels based on categorical, geographical, or physical constraints, the analysis of which aids analysts across various domains like retail, finance, and energy, in gaining valuable insights and making informed decisions. However, existing interactive exploratory analysis approaches for hierarchical time series data fall short in analyzing time series across different aggregation levels and supporting more complex analytical tasks beyond common ones like summarize and compare. These limitations motivate us to develop a new visual analytics approach. We first generalize a taxonomy to delineate various tasks in hierarchical time series analysis, derived from literature survey and expert interviews. Based on this taxonomy, we develop ChronoDeck, an interactive system that incorporates a multi-column hierarchical time series visualization for implementing various analytical tasks and distilling insights from the data. ChronoDeck visualizes each aggregation level of hierarchical time series with a combination of coordinated dimensionality reduction and small multiples visualizations, alongside interactions including highlight, align, filter, and select, assisting users in the visualization, comparison, and transformation of hierarchical time series, as well as identifying the entities of interest. The effectiveness of ChronoDeck is demonstrated by case studies on three real-world datasets and expert interviews.
Lingyu Meng, Keyi Yang, Jiabin Xu, Zikun Deng, Di Weng, Yingcai Wu
IEEE Trans. Vis. Comput. Graph.3