Browsing by Author "Cui, Weiwei"

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    Animated Presentation of Static Infographics with InfoMotion
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Wang, Yun; Gao, Yi; Huang, Ray; Cui, Weiwei; Zhang, Haidong; Zhang, Dongmei; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    By displaying visual elements logically in temporal order, animated infographics can help readers better understand layers of information expressed in an infographic. While many techniques and tools target the quick generation of static infographics, few support animation designs. We propose InfoMotion that automatically generates animated presentations of static infographics. We first conduct a survey to explore the design space of animated infographics. Based on this survey, InfoMotion extracts graphical properties of an infographic to analyze the underlying information structures; then, animation effects are applied to the visual elements in the infographic in temporal order to present the infographic. The generated animations can be used in data videos or presentations. We demonstrate the utility of InfoMotion with two example applications, including mixed-initiative animation authoring and animation recommendation. To further understand the quality of the generated animations, we conduct a user study to gather subjective feedback on the animations generated by InfoMotion.
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    Oui! Outlier Interpretation on Multi-dimensional Data via Visual Analytics
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Zhao, Xun; Cui, Weiwei; Wu, Yanhong; Zhang, Haidong; Qu, Huamin; Zhang, Dongmei; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Outliers, the data instances that do not conform with normal patterns in a dataset, are widely studied in various domains, such as cybersecurity, social analysis, and public health. By detecting and analyzing outliers, users can either gain insights into abnormal patterns or purge the data of errors. However, different domains usually have different considerations with respect to outliers. Understanding the defining characteristics of outliers is essential for users to select and filter appropriate outliers based on their domain requirements. Unfortunately, most existing work focuses on the efficiency and accuracy of outlier detection, neglecting the importance of outlier interpretation. To address these issues, we propose Oui, a visual analytic system that helps users understand, interpret, and select the outliers detected by various algorithms. We also present a usage scenario on a real dataset and a qualitative user study to demonstrate the effectiveness and usefulness of our system.