CAREER: Manipulable Semantic Components in Data Visualization Design

在Microsoft PowerPoint和Adobe Illustrator等基于矢量的设计工具中，图形对象的灵活操作是必不可少的。用户可以通过操作画布上的对象来设计复杂的视觉效果，以更改其视觉属性、应用分组和布局以及定义空间关系。理想情况下，这种交互不仅可以提高可用性和减少认知负担，还可以作为更大生态系统中无缝工作流程和设计生命周期的基石。多个人可以集体迭代设计的矢量表示，图形组件可以轻松地重新用于新的上下文。然而，在今天的交互式数据可视化设计实践中，这种交互范例很大程度上是缺失的。可视化创建者大多使用硬连接的、不灵活的模板，并且必须熟练地编码或导航复杂的界面以生成定制设计。一旦创建了可视化，就很难在不同的数据集上重复使用它，也很难将其重新用于新的上下文。这个项目研究如何在一个新的交互范例下更有效地支持数据可视化设计：可操纵语义组件(MSC)。与矢量图形设计中的可操纵图形对象类似，交互式数据可视化中的可操纵语义组件是可以在设计时(而不是运行时)可视地表示和操纵的图形对象和关联关系。该项目有可能使可视化设计大众化，并改变当前在创作、重用和协作设计数据可视化方面的做法。研究者将对可操纵语义成分(MSC)的理论框架、计算技术和应用进行研究。在第一阶段，研究人员将制定一个MSC框架，用于多变量表格、树和网络的交互可视化。该框架将描述语义组件的类型以及操作组件的操作。在第二阶段，该项目将研究新的算法和技术，实时计算由多个布局因素共同确定的组件位置，并自动解构现有的可视化以供分析和重复使用。在第三阶段，理论基础和计算技术将为新的界面和交互工具提供动力，以实现组件的灵活编辑和传输、与工具无关的实例重用和同步协作设计。为了促进研究和教育之间更紧密的结合，研究人员将使用MSC来创新设计思维的教学，并实现两个教育目标：探索设计替代方案，以及协作设计反馈和迭代。研究人员将专注于两项活动，并行原型和通过重新设计进行批评，在不同的教育背景下，包括本科生和研究生的新设计课程，以及针对行业从业者和其他学科有可视化需求的学生的外联计划。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。

Flexible manipulation of graphical objects is essential in tools like Microsoft PowerPoint and Adobe Illustrator for vector-based design. Users can design complex visuals by manipulating on-canvas objects to change their visual properties, apply grouping and layouts, and define spatial relationships. Ideally, this interaction would not only improve usability and reduce cognitive load, but also serve as a cornerstone for seamless workflows and design life cycles in a larger ecosystem. Multiple people could iterate on a design’s vector representation collectively, and the graphical components could be repurposed for new contexts with ease. Such an interaction paradigm, however, is largely missing in today’s design practices for interactive data visualization. Visualization creators mostly work with hardwired, inflexible templates, and must be proficient in coding or navigating complex interfaces to produce custom designs. Once a visualization is created, it is difficult to reuse it on a different dataset or repurpose it for new contexts. This project investigates how we can more effectively support data visualization design under a new interaction paradigm: manipulable semantic components (MSC). Similar to manipulable graphical objects in vector graphics design, manipulable semantic components in interactive data visualization are graphical objects and associated relationships that can be visually represented and manipulated at design time (instead of run time). The project has the potential to democratize visualization design and transform current practices in the authoring, reuse, and collaborative design of data visualization. The investigator will conduct research on the theoretical frameworks, computational techniques, and applications of manipulable semantic components (MSC). In the first phase, the investigator will formulate an MSC framework for interactive visualizations of multivariate tables, trees, and networks. The framework will describe both the types of semantic components and operations to manipulate the components. In the second phase, the project will investigate new algorithms and techniques that compute component positions jointly determined by multiple layout factors in real-time, and automatically deconstruct existing visualizations for analysis and reuse. In the third phase, the theoretical foundation and the computational techniques will power new interfaces and interactive tools for flexible editing and transfer of components, tool-agnostic example reuse, and synchronous collaborative design. To foster a tighter integration between research and education, the investigator will use MSC to innovate the teaching of design thinking with two education objectives: exploration of design alternatives, and collaborative design feedback and iterations. The investigator will focus on two activities, parallel prototyping, and critique by redesign, in various educational contexts including new design curricula in undergraduate and graduate classes, and outreach programs for industry practitioners and students from other disciplines with visualization needs.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.