Formal Models, Algorithms, and Visualizations for Storytelling Analytics

用于讲故事分析的形式模型、算法和可视化

• 批准号: 0937133
• 负责人: Naren Ramakrishnan
• 金额: $ 49.48万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0937133&HistoricalAwards=false
• 关键词: Formal; Models; Algorithms; Visualizations; Storytelling

Modern direct manipulation and visualization systems have made key strides in bringing powerful data transformations and algorithms to the analyst's desktop. But to further promote the vision of powerful visual analytics, wherein automated algorithms and visual representations complement each other to yield new insight, we must continually increase the expressiveness with which analysts interact with data. This project focuses on the task of storytelling, that is to say the stringing together of seemingly unconnected pieces of data into a coherent thread or argument. To support storytelling, which requires both human judgment and algorithmic assistance, the PIs will first develop a new theory of relational redescriptions that provides a uniform way to describe data and to compose data transformation algorithms across a multitude of domains. Using this theory, the PIs will be able to define stories formally as compositions of relational redescriptions. They will develop scalable and steerable algorithms for storytelling that will respond to dynamic user input, such as preferences and constraints, and they will contextualize their use in interactive visualizations that harness the power of spatial layout. Finally, they will investigate how analysts engage in sense-making using the new storytelling algorithms and visualizations, in the hope of finding answers to questions such as: How do analysts achieve insight and advance their conceptualization of patterns derived from datasets? Project outcomes will include the formal conceptualization of storytelling as well as the compositional approach to building complex chains of inference.Broader Impacts: This research will make it easier for analysts to interactively explore connections in large-scale heterogeneous datasets. The PIs will work with the FODAVA-lead team at Georgia Tech and PNNL's NVAC to investigate applications of relational redescriptions and storytelling to domains of interest to NSF and DHS, and will develop in consultation with real users across these groups a layered software framework for storytelling (both analysis and visualization) capabilities; the framework will be released into the public domain under the GNU GPL/Lesser GNU GPL license, and APIs will be provided that allow analysts to tailor it to suit their needs. Although this project will focus on cyber-analytics scenarios such as those motivated by the VAST 2009 challenge, project outcomes will generalize across other domains such as bioinformatics, systems biology, electronic commerce, and social networks. The unified notion of redescriptions will help integrate multiple data sources (numeric, symbolic, textual, and categorical), and situate them on a common footing for visual analytics; it will also enable visual analysts from different application domains to use a common vocabulary while interacting with one other.

现代直接操作和可视化系统在将强大的数据转换和算法带到分析师桌面方面取得了关键进展。 但是，为了进一步推动强大的可视化分析的愿景，即自动化算法和可视化表示相互补充以产生新的洞察力，我们必须不断提高分析师与数据交互的表现力。 这个项目的重点是讲故事的任务，也就是说，将看似无关的数据片段串在一起，形成一个连贯的线索或论点。 为了支持讲故事，这需要人类的判断和算法的帮助，PI将首先开发一种新的关系重新描述理论，提供一种统一的方法来描述数据，并在多个领域组成数据转换算法。 利用这一理论，PI将能够将故事正式定义为关系重新描述的组合。 他们将开发可扩展和可操纵的讲故事算法，这些算法将响应动态用户输入，例如偏好和约束，并且他们将在利用空间布局的力量的交互式可视化中使用它们。 最后，他们将研究分析师如何使用新的讲故事算法和可视化进行意义构建，希望找到问题的答案，例如：分析师如何实现洞察力并推进他们对数据集衍生模式的概念化？ 项目成果将包括正式的概念化的讲故事，以及组成的方法来构建复杂的推理链。更广泛的影响：这项研究将使分析师更容易交互式地探索大规模异构数据集的连接。 PI将与格鲁吉亚理工学院的FODAVA领导团队和PNNL的NVAC合作，调查关系重新描述和讲故事在NSF和DHS感兴趣的领域的应用，并将与这些群体的真实的用户协商开发用于讲故事的分层软件框架（分析和可视化）能力;该框架将在GNU GPL/Lesser GNU GPL许可证下发布到公共领域，并将提供API，允许分析人员对其进行调整以满足他们的需求。 虽然这个项目将集中在网络分析方案，如那些由VAST 2009年的挑战，项目成果将推广到其他领域，如生物信息学，系统生物学，电子商务和社交网络。 重新描述的统一概念将有助于集成多个数据源（数字，符号，文本和分类），并将它们置于可视化分析的共同基础上;它还将使来自不同应用领域的可视化分析师在相互交互时使用通用词汇表。