CRII: SCH: Multidimensional Tree Diagram Visualization for Linked Data Exploration

CRII：SCH：用于链接数据探索的多维树图可视化

• 批准号: 1657466
• 负责人: Michelle Borkin
• 金额: $ 17.48万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1657466&HistoricalAwards=false
• 关键词: CRII; SCH; Multidimensional; Tree; Diagram

The analysis and visual exploration of tree diagrams, visual representations of hierarchical data, is particularly challenging in the imaging sciences (e.g., radiology, biology, astronomy, etc.) as the tree diagrams ideally need to be viewed and explored within the context of the original data. The original data from which the tree was derived may be composed of a combination of imaging data cubes (3D volumes), 2D images, and quantitative (tabular) data. A useful technique for the visual exploration of such related data sets is linked views in which equivalent data is visually highlighted in each individual visualization. The goal of the proposed research project is to develop the novel techniques, methods, and taxonomies needed for tree diagrams to enable effective interactive visual data exploration including in the context of multidimensional linked data. The contributions of this proposal are applicable to many domains including and beyond healthcare. The new tools and taxonomies developed as part of this research, along with tutorials and syllabi, will be made freely available and shared through conferences and workshops.The research presented in this proposal will advance the state of the art in visualization through the creation of new techniques, methodologies, and taxonomies for tree diagrams. The results of the research will enable the effective design of interactive tree diagrams in a multidimensional context based on the required analytic tasks in a novel and systematic approach. The full project from taxonomy abstractions to visualization and editing techniques and methodologies to an implemented system with an evaluation case study and user testing provides a model approach to visualization research. The following research questions will be addressed as part of the proposed work: (1) How does one pick the optimal tree diagram type based on data type and analytic task? and (2) What are the best methods for interacting with tree diagrams including how to select data? To answer these questions a new taxonomy for tree diagrams will be created in order to take into account the concepts of data type, data dimensionality, quantitative data encoding options, and task. In order to enable the ability to develop linked views with tree diagrams, a new task taxonomy for tree diagrams will be created including data selection methods for tree diagrams. The taxonomies and related methodologies for tree diagrams will be implemented into an interactive publicly available tool enabling a user to select or invent the most appropriate tree diagram for their data and set of tasks, and then interact with it in a linked-view data visualization and exploration environment for visualizing and analyzing multidimensional datasets. In order to evaluate the new taxonomies and methodologies as well as the usability of the new tool incorporating these concepts, a real world case study and usability evaluation will be conducted in the healthcare field of brain imaging with the goal of investigating and developing a novel tree diagram representation of brain blood vessels.

树形图的分析和视觉探索，分层数据的视觉表示，在成像科学中特别具有挑战性（例如，放射学、生物学、天文学等）因为树图理想地需要在原始数据的上下文中被查看和探索。 导出树的原始数据可以由成像数据立方体（3D体积）、2D图像和定量（表格）数据的组合组成。 一个有用的技术，这种相关的数据集的可视化探索是链接的意见，其中等效的数据是在每个单独的可视化视觉突出显示。 拟议的研究项目的目标是开发树图所需的新技术，方法和分类，以实现有效的交互式可视化数据探索，包括在多维链接数据的背景下。 该提案的贡献适用于包括医疗保健在内的许多领域。 作为本研究的一部分开发的新工具和分类法，沿着教程和教学大纲，将通过会议和研讨会免费提供和共享。本提案中提出的研究将通过创建树图的新技术，方法和分类法来推进可视化的最新技术水平。研究的结果将使有效的设计交互式树图在多维背景下所需的分析任务的基础上，在一个新的和系统的方法。整个项目从分类抽象到可视化和编辑技术和方法，再到一个具有评估案例研究和用户测试的实现系统，为可视化研究提供了一个模型方法。 以下的研究问题将被解决作为拟议工作的一部分：（1）如何选择最佳的树图类型的数据类型和分析任务的基础上？和（2）与树图交互的最佳方法是什么，包括如何选择数据？ 为了回答这些问题，将创建一个新的树形图分类法，以考虑数据类型，数据维度，定量数据编码选项和任务的概念。 为了能够开发带有树形图的链接视图，将为树形图创建一个新的任务分类，包括树形图的数据选择方法。 树形图的分类法和相关方法将被实施为一个交互式公开工具，使用户能够选择或发明最适合其数据和任务集的树形图，然后在链接视图数据可视化和探索环境中与其交互，以可视化和分析多维数据集。 为了评估新的分类法和方法以及结合这些概念的新工具的可用性，将在脑成像的医疗保健领域进行真实的案例研究和可用性评估，目的是研究和开发一种新的脑血管树图表示。

项目成果

Digital Collaborator: Augmenting Task Abstraction in Visualization Design with Artificial Intelligence

数字协作者：利用人工智能增强可视化设计中的任务抽象

• 发表时间: 2020
• 期刊: Workshop on Artificial Intelligence for HCI: A Modern Approach (CHI 2020
• 作者: Pandey, Aditeya;Zhang, Yixuan;Guerra-Gomez, John A.;Parker, Andrea G.;Borkin, Michelle A.
• 通讯作者: Borkin, Michelle A.

CerebroVis: Designing an Abstract yet Spatially Contextualized Cerebral Artery Network Visualization

• DOI: 10.1109/tvcg.2019.2934402
• 发表时间: 2019-07
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Aditeya Pandey;H. Shukla;G. Young;Lei Qin;A. Zamani;L. Hsu;Raymond Huang;Cody Dunne;M. Borki

Towards Identification and Mitigation of Task-Based Challenges in Comparative Visualization Studies

比较可视化研究中基于任务的挑战的识别和缓解

• DOI: 10.31219/osf.io/5p73v
• 发表时间: 2020
• 期刊: 2020 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV
• 作者: Pandey, Aditeya;Syeda, Uzma H.;Borkin, Michelle A.
• 通讯作者: Borkin, Michelle A.