III: Small: Subset Selection for Summary Visualization

III：小：摘要可视化的子集选择

• 批准号: 2007436
• 负责人: Michael Gleicher
• 金额: $ 50万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007436&HistoricalAwards=false
• 关键词: III; Small; Subset; Selection; Summary

When confronted with large data sets, viewers use summaries to gain an overview of the data. For collections of complex objects, such as documents, medical records, or even chairs, standard statistical summaries either do not apply, or are difficult for viewers to interpret. Our project will develop the use of subset summaries: providing overviews of large collections by selecting a small number of data items and presenting them to the viewer. While the basic idea is straightforward, there is a lack of understanding to use summarization by subset effectively. The field lacks knowledge of how people interpret subsets to use them to accomplish overview tasks. There are no guidelines on how to choose the design of subsets, i.e., the choice of elements and their presentation. The premise of this project is that by developing a better understanding of how viewers interpret subsets, visualization creators can more appropriately choose designs to make the subsets more effective tools for summarization. The project has the potential for broad impact by providing general methods for data presentation that can be used across a wide variety of domains and through its plans for education and outreach to promote visualization proficiency and encourage a broad range of students to consider data topics. This project develop subsetting as a summarization method for overview. The work will interleave three research threads. First, the project will develop the foundations of subset summarization, adding to our understanding of how people interpret subsets to perform overview tasks. The project will use empirical studies to provide connection between design choices and task performance, which will allow us to refine theories and choose methods. Second, the project will develop a library of methods for using subsetting as a summarization method. This will include algorithms for selecting appropriate subsets, display methods to present the subsets in tabular form, and interaction techniques to move through the data set by changing the visualization subset. Development will consider approaches to embedding complex objects into metric spaces, sampling approaches for selection in these spaces, and tabular presentation and selection mechanisms to provide better user experiences. Third, the project will develop practical implementations of the methods in general toolkits and provide guidelines that advise practitioners on how to employ subset selection in their systems. The project will disseminate its results through publications, guideline documents, open source toolkits, and example applications.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.

当面对大数据集时，查看者使用摘要来获取数据的概述。对于复杂对象的集合，如文档、医疗记录，甚至椅子，标准的统计摘要要么不适用，要么很难让观众理解。我们的项目将开发子集摘要的使用：通过选择少量数据项并将其呈现给查看者来提供大型集合的概述。虽然基本思想是直截了当的，但缺乏对有效使用按子集汇总的理解。该领域缺乏人们如何解释子集以使用它们来完成概述任务的知识。关于如何选择子集的设计，即元素的选择及其呈现，没有任何准则。这个项目的前提是，通过更好地理解观众如何解释子集，可视化创建者可以更恰当地选择设计，使子集成为更有效的摘要工具。该项目有可能产生广泛的影响，因为它提供了可用于各种领域的数据展示的一般方法，并通过其教育和推广计划，促进熟练掌握可视化技术，鼓励广泛的学生考虑数据专题。本项目开发了子集作为概述的总结方法。这项工作将交织三条研究线索。首先，该项目将发展子集汇总的基础，增加我们对人们如何解释子集以执行概述任务的理解。该项目将使用实证研究来提供设计选择和任务绩效之间的联系，这将使我们能够完善理论和选择方法。其次，该项目将开发一个方法库，将子集作为一种总结方法。这将包括用于选择适当子集的算法、以表格形式呈现子集的显示方法以及通过改变可视化子集来在数据集中移动的交互技术。开发将考虑将复杂对象嵌入公制空间的方法、在这些空间中选择的抽样方法以及表格显示和选择机制，以提供更好的用户体验。第三，该项目将开发通用工具包中方法的实际实现，并提供指导方针，建议实践者如何在其系统中使用子集选择。该项目将通过出版物、指南文件、开放源码工具包和范例应用程序来传播其成果。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Trinary Tools for Continuously Valued Binary Classifiers

• DOI: 10.1016/j.visinf.2022.04.002
• 发表时间: 2022-04
• 期刊: ArXiv
• 作者: Michael Gleicher;Xinyi Yu;Yuheng Chen

A Problem Space for Designing Visualizations

设计可视化的问题空间

• DOI: 10.1109/mcg.2023.3267213
• 发表时间: 2023
• 期刊: IEEE Computer Graphics and Applications
• 影响因子: 1.8
• 作者: Gleicher, Michael;Riveiro, Maria;von Landesberger, Tatiana;Deussen, Oliver;Chang, Remco;Gillman, Christina
• 通讯作者: Gillman, Christina

Meta Design Studies: A Structured Approach for Deriving Domain-Oriented Visualization Recommendation Strategies

元设计研究：导出面向领域的可视化推荐策略的结构化方法

• 发表时间: 2023
• 期刊: IEEE Visualization Conference Poster Proceedings
• 作者: Manfred Klaffenboeck;Torsten Moeller;Michael Gleicher;Michael Wimmer
• 通讯作者: Michael Wimmer

Visual Validation versus Visual Estimation: A Study on the Average Value in Scatterplots

视觉验证与视觉估计：散点图中平均值的研究

• 发表时间: 2023
• 期刊: IEEE VIS Conference
• 作者: Daniel Braun, Ashley Suh