III: Medium: Collaborative Research: Composing Interactive Data Visualizations

III：媒介：协作研究：构建交互式数据可视化

• 批准号: 1564351
• 负责人: Joseph Hellerstein
• 金额: $ 48万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564351&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Composing

Data is a growing part of our culture, and visualizations of data are key to helping people understand and discuss the issues described by the data. Charts and graphs used to be associated with classrooms and laboratories; today they appear in mainstream coverage of weather, politics, sports, and other popular topics. This has happened in part because of advances in the technology for composing visualizations: it's much easier today to generate custom charts, thanks to a revolution in toolkits for data visualization driven by the academic research community. But it is still difficult to author *interactive* visualizations that allow users to manipulate charts. Research shows that interactivity helps people better understand and explore data visualizations. A number of interactive visualizations have appeared in popular online newspapers like the New York Times in recent years. Unfortunately, current interactive visualization toolkits are very technical and difficult to use, even for experts. The collaborative interdisciplinary team involving researchers and their students at the University of California-Berkeley (IIS-1564351), Columbia University (IIS-1564049) and University of Washington (IIS-1562182) works on making it far easier to build interactive visualizations. The goal of this project is to develop an interactive visualization design framework that will substantially simplify the task of specifying interaction in visual exploration of data. This will broaden the population of users and organizations who can craft rich, interactive visualizations and understand the presented information.The project explores a declarative approach to specifying interactive data visualizations called "Logical Interaction" (LI), realized in a new language called LIL. As a high level goal, LIL is intended to significantly simplify the specification of interactive visualizations, enabling more widespread use of interactive features in data visualizations. The dynamics of interaction introduce unique technical challenges and opportunities, including debugging and testing of asynchronous interaction handlers, and design tradeoffs between scaling up data and maintaining interface responsiveness. The hypothesis of the research is that LI can make these challenges much more tractable, and that LIL can engage visualization designers in widespread, creative development of new interactive visualizations. The research project includes exploring the fundamental modeling and language design issues in this domain, to develop techniques for composing and analyzing interaction code, and to deliver a prototype language, runtime, and analysis suite that demonstrates the benefits of our ideas. Results of the work will be embodied in a language runtime for LI, which will be freely available as open source. The project will evaluate the effectiveness of LI in terms of its interactivity and scale, the range of interactive visualizations it naturally supports, and the ability for users of varying skills to learn and use it. The researchers will also experiment with LI in university courses on Big Data and Data Science, and share the curricula publicly along with the software. Project web site (http://nsfdeclarativevis.github.io/NSFDeclarativeVis/) will provide access to project software, datasets and educational material, and research results will be published in the scientific literature.

数据是我们文化中越来越重要的一部分，数据的可视化是帮助人们理解和讨论数据所描述的问题的关键。图表和图表过去与教室和实验室有关;今天，它们出现在天气，政治，体育和其他流行话题的主流报道中。这在一定程度上是由于可视化合成技术的进步：由于学术研究社区推动的数据可视化工具包的革命，今天生成自定义图表要容易得多。但是仍然很难创作出允许用户操作图表的交互式可视化。研究表明，交互性有助于人们更好地理解和探索数据可视化。近年来，《纽约时报》等流行在线报纸上出现了许多交互式可视化。不幸的是，目前的交互式可视化工具包是非常技术性和难以使用，即使是专家。由加州大学伯克利分校（IIS-1564351）、哥伦比亚大学（IIS-1564049）和华盛顿大学（IIS-1562182）的研究人员及其学生组成的跨学科协作团队致力于使构建交互式可视化变得更加容易。这个项目的目标是开发一个交互式可视化设计框架，这将大大简化在数据的可视化探索中指定交互的任务。这将扩大用户和组织的人群，他们可以制作丰富的交互式可视化并理解所呈现的信息。该项目探索了一种声明式方法来指定交互式数据可视化，称为“逻辑交互”（LI），用一种名为LIL的新语言实现。作为一个高级目标，LIL旨在显著简化交互式可视化的规范，使交互式功能在数据可视化中得到更广泛的使用。交互的动态性带来了独特的技术挑战和机遇，包括异步交互处理程序的调试和测试，以及在扩展数据和维护接口响应之间的设计权衡。该研究的假设是，LI可以使这些挑战更加容易处理，并且LIL可以使可视化设计师参与新的交互式可视化的广泛，创造性的开发。该研究项目包括探索该领域的基本建模和语言设计问题，开发用于编写和分析交互代码的技术，并提供原型语言，运行时和分析套件，以展示我们的想法的好处。工作的结果将体现在LI的语言运行时中，该运行时将作为开源免费提供。该项目将从交互性和规模、自然支持的交互式可视化范围以及不同技能的用户学习和使用它的能力等方面评估LI的有效性。研究人员还将在大学大数据和数据科学课程中使用LI进行实验，并将课程与软件一起公开沿着。项目网站（http：//nsfdeclarativevis.github.io/NSFDeclarativeVis/）将提供项目软件、数据集和教育材料，研究结果将在科学文献中发表。