CGV: Medium: Collaborative Research: Visualizing Comparisons

CGV：媒介：协作研究：可视化比较

• 批准号: 1162037
• 负责人: Michael Gleicher
• 金额: $ 59.89万
• 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: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1162037&HistoricalAwards=false
• 关键词: CGV; Medium; Collaborative; Research; Visualizing

Comparison is an essential part of data analysis and, therefore, of many visualization tasks. While the published literature provides a wealth of visualization tools for looking at individual objects (graphs, volumes, time series, gene sequences, molecular motions, etc.), there has to date been less consideration of support for comparison. The PIs argue that comparison tasks are best supported by tools explicitly designed for that purpose. The problem is that visual comparison becomes more challenging as the number of objects, their size, and the complexity of the objects and/or of the relationships among them increases. The difficulty is further compounded by our rapidly growing ability to collect and generate data. In prior work the PIs have developed some encouraging initial examples of comparison tools, but these are specialized successes that offer little guidance for future endeavors. Addressing a wider range of comparison problems at greater scale with our present limited understanding thus largely remains an art that requires considerable effort. The PIs' goal in this project is to move towards a science of visual comparison. By studying visual comparison as a general problem, they will establish a domain-independent foundation for the field that facilitates the design of future tools which allow the creation of more effective and scalable comparisons. To these ends the team will pursue three interconnected research threads. They will define theories that are grounded upon principles of visual cognition. They will explore case studies (derived from real problems suggested by domain collaborators) that challenge and extend these theories, provide examples for empirical study, and suggest or use general concepts. And they will identify common tasks, designs, and strategies that enable development of generalized techniques, guidelines, and software components. This approach uniquely combines empirical studies, design explorations, and software development to take the field of visual comparisons to a new level that is both rooted in theory yet viable in practice. Broader Impacts: Because visual comparison plays a key role in diverse domains (including essentially all of the sciences, engineering, and medicine), the potential benefits from an improved science of visual comparison tools are far reaching. To ensure maximum applicability for project outcomes, the PIs are directly collaborating with physical, biological, social, educational, and medical scientists, as well as with engineers and scholars in the humanities. The project will generate visualization tools, software components, and resources for visualization development by others. Visual comparison will serve as a mechanism to expose students at all levels to issues in data understanding. This project will also provide training for visualization specialists, engage non-technical students in visualization, and explore the role of visualization in public outreach efforts.

比较是数据分析的重要组成部分，因此也是许多可视化任务的重要组成部分。 虽然已发表的文献提供了大量用于查看单个对象（图表、体积、时间序列、基因序列、分子运动等）的可视化工具，但迄今为止对比较的支持考虑较少。 PI 认为，比较任务最好由专门为此目的设计的工具支持。 问题在于，随着对象的数量、它们的大小以及对象和/或它们之间的关系的复杂性的增加，视觉比较变得更具挑战性。 我们收集和生成数据的能力迅速增强，这进一步加剧了困难。 在之前的工作中，PI 开发了一些令人鼓舞的比较工具的初步示例，但这些都是专门的成功案例，对未来的努力几乎没有提供指导。 因此，以我们目前有限的理解来解决更大规模的更广泛的比较问题在很大程度上仍然是一门需要大量努力的艺术。 PI 在该项目中的目标是走向视觉比较科学。 通过研究视觉比较作为一个普遍问题，他们将为该领域建立一个独立于领域的基础，促进未来工具的设计，从而允许创建更有效和可扩展的比较。 为此，该团队将追求三个相互关联的研究线索。 他们将定义基于视觉认知原理的理论。 他们将探索挑战和扩展这些理论的案例研究（源自领域合作者提出的实际问题），为实证研究提供示例，并建议或使用一般概念。 他们将确定能够开发通用技术、指南和软件组件的常见任务、设计和策略。 这种方法独特地结合了实证研究、设计探索和软件开发，将视觉比较领域提升到一个新的水平，既植根于理论，又在实践中可行。更广泛的影响：由于视觉比较在不同领域（基本上包括所有科学、工程和医学）中发挥着关键作用，因此改进视觉比较工具科学的潜在好处是深远的。 为了确保项目成果的最大适用性，PI 直接与物理、生物、社会、教育和医学科学家以及人文学科的工程师和学者合作。 该项目将生成可视化工具、软件组件和供其他人进行可视化开发的资源。 视觉比较将作为一种机制，让各个级别的学生了解数据理解方面的问题。 该项目还将为可视化专家提供培训，让非技术学生参与可视化，并探索可视化在公共宣传工作中的作用。

项目成果

Interactive Analysis of Word Vector Embeddings

• DOI: 10.1111/cgf.13417
• 发表时间: 2018-06
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: Florian Heimerl;Michael Gleicher

Perceptual Biases in Font Size as a Data Encoding

• DOI: 10.1109/tvcg.2017.2723397
• 发表时间: 2018-08
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: E. Alexander;Chih-Ching Chang;Mariana Shimabukuro;S. Franconeri;C. Collins;Michael Gleicher

Scatterplots: Tasks, Data, and Designs

• DOI: 10.1109/tvcg.2017.2744184
• 发表时间: 2018
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Alper Sarıkaya;Michael Gleicher

Design Factors for Summary Visualization in Visual Analytics

视觉分析中摘要可视化的设计因素

• DOI: 10.1111/cgf.13408
• 发表时间: 2018
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: Sarikaya, A.;Gleicher, M.;Szafir, D. A.
• 通讯作者: Szafir, D. A.

Visual Exploration of Word Vector Embeddings

• 发表时间: 2017
• 作者: Florian Heimerl;Michael Gleicher