CAREER: Visual Analytics by Demonstration for Interactive Data Analysis

在今天这个数据驱动的时代，越来越多的人将分析数据作为他们日常生活的一部分。可视化分析技术可以帮助各种领域的人们，包括商业、健康、教育、国家安全等等。这些可视化分析技术是有效的，因为它们将强大的机器学习与交互式数据可视化相结合。目前，人们使用这种系统的方式是通过带有控制面板的用户界面来直接调整分析模型的参数和可视化属性。例如，控件可能要求用户选择使用哪个分析模型，调整哪个参数，以及调整多少。然而，当人们在数据科学或信息可视化方面没有必要的专业知识或培训时，他们可能无法正确使用这些工具，并错过重要的见解。相反，这个项目将探索、设计、开发和评估允许人们演示他们的分析目标、任务和操作的技术。开发这种“通过演示进行可视化分析”的用户交互方法有可能影响许多数据驱动的领域，以及更广泛的社会。该项目还将为研究生和本科生提供教育经验和研究培训，以指导他们从事计算机职业。该研究将通过演示原型、通用工具包和演示原语来创建可视化分析，以促进可视化分析的探索和发现。人们提供演示，而不是要求用户直接参数化分析模型和可视化的控制面板，系统从中选择适当的可视化表示、分析模型和参数。为了实现这种技术的好处，存在许多研究挑战，必须加以解决。例如，人们用来向系统传达意图的基本演示原语是什么？系统如何解释这些演示并执行正确的分析和可视化操作？最后，这些系统如何防止潜在的用户在使用演示来探索数据时的偏见？项目目标包括通过演示可视化分析原型的设计、实现和评估。该研究将执行形成性研究，以开发按用户任务分类的演示原语。该项目将设计和开发特定数据领域的应用程序，以及通用的开源工具包，供其他研究人员使用和扩展。用户研究将确定是否以及如何通过演示来执行特定的任务和操作，以及如何将这些任务和操作与当前控制面板界面的性能进行比较。拟议的工作还将创建教学材料，通过演示将可视化分析整合到可视化分析或更广泛的数据科学的课程中。项目网站（http://va.gatech.edu/projects/visual-analytics-by-demonstration/）将包括项目信息、相关出版物、视频和开源软件的链接。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In today's data-driven era, more people analyze data as part of their daily lives. Visual analytic technologies help people in a variety of these domains including business, health, education, national security, and many more. These visual analytic techniques are effective since they combine powerful machine learning with interactive data visualization. Currently, the way that people use such systems is through user interfaces with control panels to adjust parameters of the analytic models and visualization properties directly. For example, controls may ask users to select which analytic model to use, which parameter to adjust, and by how much. However, when people do not have the necessary expertise or training in data science or information visualization, they may not be able to properly use these tools and miss out on important insights. Instead, this project will explore, design, develop, and evaluate techniques that allow people to demonstrate their analytic goals, tasks, and operations. Developing this "visual analytics by demonstration" method of user interaction has the potential to impact numerous data-driven domains, and society more broadly. This project will also provide educational experience and research training for graduate and undergraduate students to guide them towards careers in computing.The proposed research will create Visual Analytics by Demonstration prototypes, generalizable toolkits, and demonstration primitives to foster exploration and discovery in visual analytics. Instead of control panels that require users to directly parameterize analytic models and visualizations, people provide demonstrations, from which the system selects the appropriate visual representation, analytic model, and parameters. To realize the benefits of such technology, many research challenges exist and must be addressed. For instance, what are the basic demonstration primitives that people use to communicate their intent to a system? How can systems interpret these demonstrations and perform the correct analytic and visualization operations? Finally, how can these systems guard against potential user bias in exploring data using by-demonstration? Project objectives include the design, implementation, and evaluation of by-demonstration visual analytic prototypes. The research will perform formative studies to develop demonstration primitives categorized by user tasks. The project will design and develop applications for specific data domains as well as general, open-sourced toolkits for other researchers to use and extend. User studies will identify if and how specific tasks and operations can be performed by demonstrations, and how these compare in performance to current control panel interfaces. The proposed work will also create instructional material to integrate visual analytics by demonstration into courses that teach visual analytics, or data science more generally. The project website (http://va.gatech.edu/projects/visual-analytics-by-demonstration/) will include project information, links to resulting publications, videos, and open-source software produced.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.