Formal Models, Algorithms, and Visualizations for Storytelling Analytics

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的桌面方面取得了关键进展。但是，为了进一步促进强大的可视化分析的愿景，其中自动算法和可视化表示相互补充以产生新的见解，我们必须不断增加分析师与数据交互的表达能力。这个项目的重点是讲故事的任务，也就是说，把看似不相关的数据片段串在一起，形成一个连贯的线索或论点。为了支持需要人工判断和算法辅助的故事叙述，pi将首先开发一种新的关系重描述理论，该理论提供了一种统一的方法来描述数据并组合跨多个领域的数据转换算法。使用这一理论，pi将能够将故事正式定义为关系重描述的组合。他们将开发可扩展和可控制的算法，用于讲故事，以响应动态用户输入，例如偏好和约束，他们将在利用空间布局的力量的交互式可视化中使用这些算法。最后，他们将调查分析师如何使用新的讲故事算法和可视化来进行意义构建，希望找到以下问题的答案：分析师如何获得洞察力并推进他们对源自数据集的模式的概念化？项目成果将包括讲故事的正式概念化，以及构建复杂推理链的组合方法。更广泛的影响：这项研究将使分析人员更容易交互式地探索大规模异构数据集中的联系。PIs将与佐治亚理工学院fodava领导的团队和PNNL的NVAC合作，研究关系重描述和故事叙述在NSF和DHS感兴趣的领域的应用，并将与这些群体的实际用户协商，开发一个故事叙述（分析和可视化）能力的分层软件框架；该框架将在GNU GPL/Lesser GNU GPL许可下发布到公共领域，并且将提供api，允许分析人员根据他们的需要对其进行定制。尽管该项目将侧重于网络分析方案，如由VAST 2009挑战赛所激发的方案，但项目成果将推广到其他领域，如生物信息学、系统生物学、电子商务和社会网络。重新描述的统一概念将有助于集成多个数据源（数字的、符号的、文本的和分类的），并将它们放在一个共同的基础上进行可视化分析；它还将使来自不同应用程序领域的可视化分析人员能够在相互交互时使用公共词汇表。

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.