CRII: CHS: Concept-Driven Visual Analysis

CRII：CHS：概念驱动的可视化分析

• 批准号: 1755611
• 负责人: Khairi Reda
• 金额: $ 17.5万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755611&HistoricalAwards=false
• 关键词: CRII; CHS; Concept; Driven; Visual

Tools that create visualizations, or visual representations of large datasets, are increasingly important for making use of data in a number of domains from commerce to science. To date, most visualization tools have been designed to support open-ended exploration of patterns in data; though useful for some tasks, this exploratory model does not fit well when analysts have existing models or hypotheses. This project aims to support a "concept-driven" analysis style in which analysts can share their existing conceptual models with the system, which uses those models to generate visualizations that allow the analyst to explore places where the models and data disagree and develop revised models that reconcile those discrepancies. To do this, the research team will design a number of prototype techniques for communicating conceptual models, algorithms for selecting visualizations and data features that best match those models, and interfaces that highlight discrepancies and provide tools for analysts to dig into the data around them. If successful, these concept-driven analyses will provide better ways for scientists and other analysts with existing models to leverage data while reducing the risk of confirmation biases in which people choose analyses that don't show where their existing models are wrong. The project will also enable the research team to learn more about the ways people come to form and express expectations about data. Lastly, project will provide opportunities for graduate research training as well as tools to support K-12 outreach workshops that introduce younger students to data science.The project has two main activities. The first involves prototyping three elicitation techniques that prompt users to externalize their mental models and expectations about a dataset: free text expressions combined with natural language processing techniques that extract both variables of interest and implied relationships between them; concept mapping tools that allow users to graphically express relationships between entities, ideas, and concepts as node-link diagrams in which the nodes represent key aspects of the data and links represent suspected relationships between them; and tools for sketching expected relationships between variables using existing visualizations such as line charts and heatmaps. The team will also develop interfaces that encourage analysts to develop several alternative models to reduce the chance of confirmation bias. The second main activity is using the captured models to generate relevant visualizations that support discrepancy exploration. To do this, the team will first use a taxonomy of best practices for choosing visualizations that best fit the concepts and relationships represented in the models. They will then design interfaces that highlight discrepancies in both the visualizations (for instance, by highlighting data that badly fits a model) and the models (for instance, by highlighting links in a concept map that are not supported by the data) to call attention to inconsistencies. Both the elicitation and feedback interfaces will be refined through a series of semi-structured visual analysis studies in which participants use them to analyze data in domains of general interest such as socioeconomic indices, crime statistics, and health risks. The refined versions will then be used to compare the effectiveness of the concept-driven approach with more traditional exploratory approaches, as well as against both structured and unstructured workflows that interleave exploratory and concept-driven elements, in a series of lab studies using participants drawn from a number of scientific disciplines and a case study with scientific partners at Argonne National Laboratory.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

创建可视化或大型数据集的视觉表示的工具对于利用从商业到科学的许多领域中的数据变得越来越重要。 迄今为止，大多数可视化工具都是为了支持对数据模式的开放式探索而设计的。尽管对于某些任务很有用，但当分析师已有模型或假设时，这种探索性模型就不太适合。 该项目旨在支持“概念驱动”的分析风格，其中分析师可以与系统共享他们现有的概念模型，系统使用这些模型生成可视化效果，使分析师能够探索模型和数据不一致的地方并开发修订模型来调和这些差异。 为此，研究团队将设计许多用于传达概念模型的原型技术、用于选择与这些模型最匹配的可视化和数据特征的算法，以及突出差异并为分析师提供挖掘周围数据的工具的界面。 如果成功，这些概念驱动的分析将为科学家和其他分析师利用现有模型利用数据提供更好的方法，同时降低确认偏差的风险，即人们选择的分析不会显示现有模型的错误之处。该项目还将使研究团队能够更多地了解人们形成和表达对数据的期望的方式。 最后，该项目将为研究生研究培训提供机会，并提供支持 K-12 外展研讨会的工具，向年轻学生介绍数据科学。该项目有两项主要活动。 第一个涉及原型三种启发技术，提示用户将他们的心理模型和对数据集的期望具体化：自由文本表达与自然语言处理技术相结合，提取感兴趣的变量及其之间的隐含关系；概念图工具，允许用户以图形方式将实体、想法和概念之间的关系表达为节点链接图，其中节点代表数据的关键方面，链接代表它们之间的可疑关系；以及使用现有可视化（例如折线图和热图）绘制变量之间预期关系的工具。 该团队还将开发界面，鼓励分析师开发几种替代模型，以减少确认偏差的可能性。 第二个主要活动是使用捕获的模型生成支持差异探索的相关可视化效果。 为此，团队将首先使用最佳实践分类来选择最适合模型中表示的概念和关系的可视化。 然后，他们将设计界面，突出显示可视化（例如，通过突出显示不适合模型的数据）和模型（例如，通过突出显示概念图中不受数据支持的链接）之间的差异，以引起注意到不一致的情况。 启发和反馈界面都将通过一系列半结构化视觉分析研究进行完善，参与者使用它们来分析社会经济指数、犯罪统计和健康风险等普遍感兴趣领域的数据。 然后，在使用抽取的参与者进行的一系列实验室研究中，改进的版本将用于比较概念驱动方法与更传统的探索性方法的有效性，以及与交叉探索性和概念驱动元素的结构化和非结构化工作流程的有效性。来自多个科学学科以及与阿贡国家实验室的科学合作伙伴进行的案例研究。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Concept-Driven Visual Analytics: an Exploratory Study of Model- and Hypothesis-Based Reasoning with Visualizations

概念驱动的视觉分析：基于模型和假设的可视化推理的探索性研究

• DOI: 10.1145/3290605.3300298
• 发表时间: 2019
• 期刊: CHI'19: ACM Conference on Human Factors in Computing Systems
• 作者: Choi, In Kwon;Childers, Taylor;Raveendranath, Nirmal Kumar;Mishra, Swati;Harris, Kyle;Reda, Khairi
• 通讯作者: Reda, Khairi

Dynamic Glyphs: Appropriating Causality Perception in Multivariate Visual Analysis

动态字形：多元视觉分析中适当的因果关系感知

• DOI: 10.31219/osf.io/s5q9n
• 发表时间: 2019
• 期刊: Vis x Vision Workshop: Novel Directions in Vision Science and Visualization Research
• 作者: Reda, Khairi;Potts, Caleb;Childers, Taylor
• 通讯作者: Childers, Taylor

Pushing the (Visual) Narrative: the Effects of Prior Knowledge Elicitation in Provocative Topics

推动（视觉）叙事：先验知识启发对挑衅性主题的影响

• DOI: 10.1145/3313831.3376887
• 发表时间: 2020
• 期刊: CHI'20: ACM Conference on Human Factors in Computing Systems
• 作者: Heyer, Jeremy;Raveendranath, Nirmal Kumar;Reda, Khairi
• 通讯作者: Reda, Khairi

Visual (dis)Confirmation: Validating Models and Hypotheses with Visualizations

视觉（反）确认：通过可视化验证模型和假设

• DOI: 10.1109/iv-2.2019.00032
• 发表时间: 2019
• 期刊: 23rd International Conference in Information Visualization – Part II
• 作者: Choi, In Kwon;Raveendranath, Nirmal Kumar;Westerfield, Jared;Reda, Khairi
• 通讯作者: Reda, Khairi

Towards Concept-Driven Visual Analytics

迈向概念驱动的可视化分析

• 发表时间: 2018
• 期刊: IEEE Conference on Visual Analytics Science and Technology (extended abstract
• 作者: Choi, In Kwon;Mishra, Swati;Harris, Kyle;Raveendranath, Nirmal Kumar;Childers, Taylor;Reda, Khairi
• 通讯作者: Reda, Khairi