EAGER: Collaborative Research: Articulate: Augmenting Data Visualization With Natural Language Interaction

EAGER：协作研究：清晰表达：通过自然语言交互增强数据可视化

• 批准号: 1445751
• 负责人: Barbara DiEugenio
• 金额: $ 24.15万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1445751&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Articulate; Augmenting

Nearly one third of the human brain is devoted to processing visual information. Vision is the dominant sense for the acquisition of information from our everyday world. It is therefore no surprise that visualization, even in its simplest forms, remains the most effective means for converting large volumes of raw data into insight, a process that can support scientific discovery. However a key challenge hindering scientific users from adopting the latest visualization tools and techniques is the steep learning curve that has to be overcome in order to make use of them. The tendency then is to resort to the simplest tools, such as bar charts and line graphs, even though they may lack the expressive power necessary to bring scientific data into focus.The notion that scientists would ideally like to simply speak with a computer to ask questions about their data, and have the computer automatically generate visualizations that answer their queries, has been well known since at least the NSF 2007 report "Enabling Science Discoveries through Visual Exploration." This is the motivation for the current project, which involves a collaboration among researchers at two institutions, given that scientists still are unable to do so. The PIs' ultimate goal is to implement a Virtual Visualization Expert to translate the language of science into the language of visualization. To demonstrate the concept is indeed viable, the PIs previously developed and evaluated a small prototype, which supported their argument that by relieving the user of the burden of having to learn how to use a complex interface one could enable them to focus on articulating better scientific questions.Given this initial success, the focus of this exploratory research is to establish the foundations of a more generalizable approach that can encompass techniques used in scientific visualization. To this end, the PIs will research the steps needed for mapping natural language requests, which may be accompanied by gestures, into meaningful visualizations and for enabling incremental creation and modifications of visualizations. They will develop innovative models to understand the intent of the user and the objects s/he is referring to, and they will explore how best to design user interfaces for creating and modifying visualizations using language and direct manipulation. The PIs' initial study showed that all these capabilities are crucial to enabling users to make the best use of a dialogic interface for data visualization. Although project outcomes will be geared in the short term to serving the scientific community, the techniques should be applicable more broadly to consumers of information, such as citizen scientists, public policy decision makers, and students.

近三分之一的人大脑致力于处理视觉信息。视觉是从我们日常世界中获取信息的主要意义。 因此，毫不奇怪的是，即使以最简单的形式，可视化仍然是将大量原始数据转换为洞察力的最有效手段，这一过程可以支持科学发现。但是，阻碍科学用户采用最新可视化工具和技术的主要挑战是必须克服的陡峭学习曲线才能使用它们。 The tendency then is to resort to the simplest tools, such as bar charts and line graphs, even though they may lack the expressive power necessary to bring scientific data into focus.The notion that scientists would ideally like to simply speak with a computer to ask questions about their data, and have the computer automatically generate visualizations that answer their queries, has been well known since at least the NSF 2007 report "Enabling Science Discoveries through Visual Exploration." 这是当前项目的动机，涉及两个机构研究人员之间的合作，因为科学家仍然无法做到这一点。 PI的最终目标是实施虚拟可视化专家，将科学的语言转化为可视化语言。 为了证明这一概念的确是可行的，PI先前开发并评估了一个小的原型，这支持了他们的论点，即通过使用户减轻必须学习如何使用复杂界面的负担，可以使他们能够专注于更好的科学问题，从而确定这种探索性研究的重点，该探索性研究的重点是建立更具常规技术的基础。 为此，PI将研究映射自然语言请求所需的步骤，这些步骤可能伴随着手势，将其映射到有意义的可视化中，并启用增量创建和可视化的修改。 他们将开发创新的模型，以了解用户的意图以及他所指的对象，并将探索如何最好地设计用户界面，以使用语言和直接操作来创建和修改可视化。 PIS的初步研究表明，所有这些功能对于使用户能够充分利用对话界面进行数据可视化至关重要。 尽管项目成果将在短期内为科学界服务，但这些技术应更广泛地适用于信息消费者，例如公民科学家，公共政策决策者和学生。