检索结果-内蒙古大学图书馆

Breaking the Fourth Wall of data Stories through Interaction

IEEE Transactions on visualization and Computer Graphics 2023年第1期29卷 972-982页

作者： Shi, Yang Gao, Tian Jiao, Xiaohan Cao, Nan Tongji University Intelligent Big Data Visualization Lab China

Interaction is increasingly integrating into data stories to support data exploration and explanation. Interaction can also be combined with the narrative device, breaking the fourth wall (BTFW), to build a deeper connection between readers and data stories. BTFW interaction directly addresses readers by requiring their input. Such user input is then integrated into the narrative or visuals of data stories to encourage readers to inspect the stories more closely. In this work, we explore the design patterns of BTFW interaction commonly used in data stories. Six design patterns were identified through the analysis of 58 high-quality data stories collected from a range of online sources. Specifically, the data stories were categorized using a coding framework, including the input of BTFW interaction provided by readers and the output of BTFW interaction generated by data stories to respond to the input. To explore the benefits as well as concerns of using BTFW interaction, we conducted a three-session user study including the reading, interview, and recall sessions. The results of our user study suggested that BTFW interaction has a positive impact on self-story connection, user engagement, and information recall. We also discussed design implications to address the possible negative effects on the interactivity-comprehensibility balance, information privacy, and the learning curve of interaction brought by BTFW interaction. © 2022 IEEE.

关键词： data visualization

来源：评论

学校读者我要写书评

暂无评论

Supporting Expressive and Faithful Pictorial visualization Design with Visual Style Transfer

引用

IEEE Transactions on visualization and Computer Graphics 2023年第1期29卷 236-246页

作者： Shi, Yang Liu, Pei Chen, Siji Sun, Mengdi Cao, Nan Tongji University Intelligent Big Data Visualization Lab China

Pictorial visualizations portray data with figurative messages and approximate the audience to the visualization. Previous research on pictorial visualizations has developed authoring tools or generation systems, but their methods are restricted to specific visualization types and templates. Instead, we propose to augment pictorial visualization authoring with visual style transfer, enabling a more extensible approach to visualization design. To explore this, our work presents Vistylist, a design support tool that disentangles the visual style of a source pictorial visualization from its content and transfers the visual style to one or more intended pictorial visualizations. We evaluated Vistylist through a survey of example pictorial visualizations, a controlled user study, and a series of expert interviews. The results of our evaluation indicated that Vistylist is useful for creating expressive and faithful pictorial visualizations. © 2022 IEEE.

关键词： data visualization

来源：评论

学校读者我要写书评

暂无评论

Understanding Design Collaboration Between Designers and Artificial Intelligence: A Systematic Literature Review

引用

Proceedings of the ACM on Human-Computer Interaction 2023年第CSCW2期7卷 1-35页

作者： Shi, Yang Gao, Tian Jiao, Xiaohan Cao, Nan Intelligent Big Data Visualization Lab Tongji University Shanghai China

Recent interest in design through the artificial intelligence (AI) lens is rapidly increasing. Designers, as a special user group interacting with AI, have received more attention in the Human-Computer Interaction community. Prior work has discussed emerging challenges that persist in designing for AI. However, few systematic reviews focus on AI for design to understand how designers and AI can augment each other's complementary strengths in design collaboration. In this work, we conducted a landscape analysis of AI for design, via a systematic literature review of 93 papers. The analysis first provides a bird's eye view of overall patterns in this area. The analysis also reveals three themes interpreted from the paper corpus associated with AI for design, including AI assisting designers, designers assisting AI, and characterizing designer-AI collaboration. We discuss the implications of our findings and suggested methodological proposals to guide HCI toward research and practices that center on collaborative creativity. © 2023 ACM.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Erato: Cooperative data Story Editing via Fact Interpolation

引用

IEEE Transactions on visualization and Computer Graphics 2023年第1期29卷 983-993页

作者： Sun, Mengdi Cai, Ligan Cui, Weiwei Wu, Yanqiu Shi, Yang Cao, Nan Tongji University Intelligent Big Data Visualization Lab China Microsoft Research Asia China

As an effective form of narrative visualization, visual data stories are widely used in data-driven storytelling to communicate complex insights and support data understanding. Although important, they are difficult to create, as a variety of interdisciplinary skills, such as data analysis and design, are required. In this work, we introduce Erato, a human-machine cooperative data story editing system, which allows users to generate insightful and fluent data stories together with the computer. Specifically, Erato only requires a number of keyframes provided by the user to briefly describe the topic and structure of a data story. Meanwhile, our system leverages a novel interpolation algorithm to help users insert intermediate frames between the keyframes to smooth the transition. We evaluated the effectiveness and usefulness of the Erato system via a series of evaluations including a Turing test, a controlled user study, a performance validation, and interviews with three expert users. The evaluation results showed that the proposed interpolation technique was able to generate coherent story content and help users create data stories more efficiently. © 2022 IEEE.

关键词： data visualization

来源：评论

学校读者我要写书评

暂无评论

Double Tap for This Post: Understanding the Communication of data visualization on Social Media

引用

Proceedings of the ACM on Human-Computer Interaction 2025年第2期9卷 1-27页

作者： Shi, Yang Peng, Yechun Ding, Jieying Lan, Xingyu Cao, Nan Intelligent Big Data Visualization Lab Tongji University Shanghai China School of Journalism Fudan University Shanghai China

data visualizations are increasingly used by news outlets on social media to communicate insights to a broad audience. However, little is known about how readers interact with and respond to data visualizations in these quick-consumption environments. In this work, we introduce a conceptual model that categorizes visualization reading that leads to the communication effect of likes on Instagram. The model was developed through a grounded theory analysis of the statements explaining the reasoning behind the likes of visualization, which were recorded from a preliminary study. Informed by coding the statements from two dimensions including scopes and design patterns concerning visualization, our model consists of three levels: depicting the "look"of a visualization (e.g., artistic style and color scheme);interpreting the "flesh and bones"of a visualization (e.g., visualization and narrative);and elucidating the "heart and soul"of a visualization (e.g., insights and conclusion). We also conducted an online crowdsourcing user study with 200 participants to demonstrate how our model can be applied to improve the communication of visualization by comparing the three levels. © 2025 ACM.

关键词： data visualization

来源：评论

学校读者我要写书评

暂无评论

AVA:An automated and AI-driven intelligent visual analytics framework

引用

Visual Informatics 2024年第2期8卷 106-114页

作者： Jiazhe Wang Xi Li Chenlu Li Di Peng Arran Zeyu Wang Yuhui Gu Xingui Lai Haifeng Zhang Xinyue Xu Xiaoqing Dong Zhifeng Lin Jiehui Zhou Xingyu Liu Wei Chen Intelligent Big Data Visualization Lab at Tongji University ShanghaiChina Ant Group China Alibaba Group China State Key Lab of CAD&CG Zhejiang UniversityHangzhouChina

With the incredible growth of the scale and complexity of datasets,creating proper visualizations for users becomes more and more challenging in large *** several visualization recommendation systems have been proposed,so far,the lack of practical engineering inputs is still a major concern regarding the usage of visualization recommendations in the *** this paper,we proposed AVA,an open-sourced web-based framework for Automated Visual *** contains both empiric-driven and insight-driven visualization recommendation methods to meet the demands of creating aesthetic visualizations and understanding expressible insights *** code is available at https://***/antvis/AVA.

关键词： Automated visual analytics visualization recommendation Insight mining

来源：评论

学校读者我要写书评

暂无评论

Utilizing TimeArcs and PageRank to Solve the 2024 VAST Challenge Mini Challenge 3

Utilizing TimeArcs and PageRank to Solve the 2024 VAST Chall...

引用

2024 IEEE Visual Analytics Science and Technology VAST Challenge, Vast-Challenge 2024

作者： Wei, Ethan Dang, Tommy Texas Tech University United States Texas Tech University Interactive Data Visualization Lab United States

ISBN: (纸本)9798331517274

To solve the 2024 VAST Challenge MC3, we use PageRank and different filtering techniques to select nodes or components of interest. We then use TimeArc, a data visualization technique to visualize the evolution of the corporate structure of these nodes and serve as a tool to investigate and confirm this suspicious behavior. We used these techniques to investigate many nodes including the given SouthSeafood Express Corp that was involved in illegal activity. We discovered a few key features associated with anomalous nodes such as instances of founding shell companies and large power transfers. © 2024 IEEE.

关键词： Anomaly Detection Mini Challenge 3 VAST Challenge

来源：评论

学校读者我要写书评

暂无评论

Visual evaluation of graph representation learning based on the presentation of community structures

引用

Visual Informatics 2024年第3期8卷 29-31页

作者： Yong Zhang Lihong Cai Yuhua Liu Yize Li Songyue Li Yuming Ma Yuwei Meng Zhiguang Zhou Intelligent Big Data Visualization Lab Hangzhou Dianzi UniversityHangzhouChina Science&Informatization Department Zhejiang Provincial Energy Group CompanyHangzhouChina State Key Lab of CAD&CG Zhejiang UniversityHangzhouChina

Various graph representation learning models convert graph nodes into vectors using techniques like matrix factorization,random walk,and deep ***,choosing the right method for different tasks can be *** within networks help reveal underlying structures and *** how different models preserve community properties is crucial for identifying the best graph representation for data *** paper defines indicators to explore the perceptual quality of community properties in representation learning spaces,including the consistency of community structure,node distribution within and between communities,and central node distribution.A visualization system presents these indicators,allowing users to evaluate models based on community *** studies demonstrate the effectiveness of the indicators for the visual evaluation of graph representation learning models.

关键词： Graph representation learning Community structure Visual perception

来源：评论

学校读者我要写书评

暂无评论

ColorCook: Augmenting Color Design for Dashboarding with Domain-Associated Palettes

引用

Proceedings of the ACM on Human-Computer Interaction 2022年第CSCW2期6卷 1-25页

作者： Shi, Yang Chen, Siji Liu, Pei Long, Jiang Cao, Nan Intelligent Big Data Visualization Lab Tongji University Shanghai China Huawei Technologies Co. Ltd. Hangzhou China

visualization dashboards serve as an information presentation that uses a tiled layout of key metrics visualized in charts for collaborative decision-making. Existing work has developed tools and techniques for computational color design. Much of these efforts have focused on selecting effective color palettes for independent charts while few attempts have been made to support the expressive color design of multiple coordinated charts in dashboards. In this work, we describe ColorCook, an interactive system that helps design expressive and effective dashboard colorings using domain-Associated palettes. ColorCook employs an integrated color workflow for dashboarding, consisting of color selection, assignment, and adjustment. We evaluated ColorCook through a crowdsourcing experiment and a user study. The results of our evaluation indicated that ColorCook is useful for effective and expressive color design. © 2022 ACM.

关键词： visualization

来源：评论

学校读者我要写书评

暂无评论

How Does Emotion Affect Information Communication

arXiv

引用

arXiv 2025年

作者： Cao, Shixiong Cao, Nan Intelligent Big Data Visualization Lab Tongji University China

In recent years, the pivotal role of emotions in information dissemination has attracted extensive attention. Research indicates that emotionally charged content significantly outperforms neutral content in dissemination speed and coverage due to its ability to evoke stronger emotional responses. Valence, arousal, and dominance are key factors influencing information retention, comprehension, and sharing. However, systematic analysis of the mechanisms by which emotions affect dissemination and the application of multidimensional emotional design remain limited. This study reviews 166 relevant articles, examining the role of emotions in understanding and sharing information, and proposes a multidimensional design framework based on text, visuals, sound, and interaction design. Incorporating the "Four-System" model of emotion activation, it develops an emotion regulation framework to optimize the application of emotions in dissemination. This research offers theoretical support and practical guidance for emotion-driven dissemination, laying a foundation for advancing this field. Copyright © 2025, The Authors. All rights reserved.

关键词： Reviews

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：