EAGER: Collaborative Visualization for Knowledge Computing

这项工作提出了新的方法来协同可视化和交互式探索大型互连的高维数据集表示为大型非结构化图形。拟议的工作建立在可扩展的可视化方法，能够支持交互式探索超过10万个节点的标准台式计算机上，即使不使用层次聚类。本研究在以下三个研究方向上推进了交互式网络可视化的最新技术水平，这些研究方向是面向社交网络数据的协作交互式意义构建的可视化工具集，表示为互连图：基于Web的可扩展性，协作图分析和新的可视化范式，所有这些都有助于从语义网（和语义网）对大型图进行可视化和协作比较。这些新的可视化技术的一个具体重点是可视化的不确定性与数据源或inferences.The智力这项工作的优点是建立了新的贡献领域的信息和科学可视化，以及人机交互：交互式协作操纵和分析非常大的图形结构是目前不可能与现有的工具和技术。我们提出的研究提供了一个新的协作和用户驱动的社会网络分析的角度是互动的，灵活的，可扩展的和可扩展的，足以跟上社会网络的快速扩张。预期的结果，一种新的拖放方法，用于缩小规模，测试，比较和评估信息网络，将建立和评估新的机制，以管理网络信息的冲击，以协作的方式。结果将通过在基于语义网的分析和交互基础设施中集成和评估新的可视化和交互技术来展示，拟议的工作将通过开源语义网平台向广大受众提供创新的可扩展图形可视化方法，从而产生更广泛的影响。网络受众将能够直接在浏览器中访问最先进的图形分析和可视化工具，而无需下载任何类型的applet、插件或虚拟机。PI将使用拟议的研究作为案例研究和支持人机交互基础教学的项目平台。跨学科性是成功的用户界面技术项目的基石，如这一个，和调查员的研究小组已证明致力于与校园内的其他部门的合作和伙伴关系，以及来自行业和公众社区的代表，针对研究成果的广泛传播。

This work proposes novel approaches to collaboratively visualize and interactively explore large interconnected high-dimensional data sets represented as large unstructured graphs. The proposed work builds upon scalable visualization methodology that is able to support interactive exploration of over a hundred thousand nodes on standard desktop computers, even without the use of hierarchical clustering. This research advances the state of the art in interactive network visualization in the following three research directions towards a visual tool set for collaborative interactive sense-making of social network data, represented as interconnected graphs: Web-based Scalability, Collaborative Graph Analysis, and New Visualization Paradigms, all together facilitating the visualization and collaborative comparison of large graphs from (and over) the semantic web. A specific focus of these novel visualization techniques is the visualization of uncertainty associated with the data sources or inferences.The intellectual merit of this work is established by novel contributions to the fields of information and scientific visualization, as well as human-computer interaction: Interactive collaborative manipulation and analysis of very large graph structures is not currently possible with existing tools and techniques. Our proposed research provides a new collaborative and user-driven perspective on social network analysis which is interactive, flexible, scalable and extensible enough to keep pace with the rapid expansion of the social web. The intended results, a novel drag-and-drop approach for down-scaling, testing, comparing, and evaluating information networks, will establish and evaluate novel mechanisms to manage the onslaught of networked information in a collaborative manner. Results will be demonstrated through integration and evaluation of the novel visualization and interaction techniques within a semantic web-based analysis and interaction infrastructure.The proposed work will enable significant broader impacts by making innovative scalable graph visualization methodologies available to broad audiences via open-source semantic web platforms. Web audiences will be enabled to access novel state-of-the-art graph analysis and visualization tools directly in their browsers without the need for downloading applets, plugins, or virtual machines of any kind. The PI will be using the proposed research as a case study and platform for projects supporting the teaching of human-computer interaction fundamentals. Interdisciplinarity is a cornerstone of successful user interface technology projects such as this one, and the investigator's research group has a demonstrated commitment to collaborations and partnerships with other departments on campus, as well as representatives from industry and the public community, targeting broad dissemination of the research results.