EAGER: Collaborative Visualization for Knowledge Computing

这项工作提出了新的方法来协作可视化和交互地探索表示为大型非结构化图的大型相互关联的高维数据集。建议的工作建立在可扩展的可视化方法的基础上，该方法能够支持在标准台式计算机上交互探索超过10万个节点，即使不使用分层集群。这项研究在以下三个研究方向推动了交互式网络可视化的发展：基于Web的可扩展性、协作图分析和新的可视化范例，所有这些都促进了来自(或超过)语义网的大图的可视化和协作比较。这些新的可视化技术的一个具体焦点是与数据源或推理相关的不确定性的可视化。这项工作的智力价值是通过对信息和科学可视化以及人机交互领域的新贡献来确立的：目前，使用现有的工具和技术无法对非常大的图形结构进行交互式协作操作和分析。我们提出的研究为社交网络分析提供了一种新的协作和用户驱动的视角，它具有交互性、灵活性、可伸缩性和可扩展性，足以跟上社交网络的快速扩张。预期的结果是一种新的拖放方法，用于缩小、测试、比较和评估信息网络，将建立和评估以协作方式管理网络信息冲击的新机制。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放源码的语义网平台向广大受众提供创新的可伸缩的图形可视化方法，从而产生重大的更广泛的影响。网络受众将能够在他们的浏览器中直接访问新的最先进的图形分析和可视化工具，而不需要下载任何类型的小程序、插件或虚拟机。PI将把拟议的研究作为支持人机交互基础教学的项目的案例研究和平台。跨学科是成功的用户界面技术项目的基石，如这个项目，调查员的研究小组已表明致力于与校园内的其他部门以及来自行业和公众社区的代表合作和伙伴关系，旨在广泛传播研究结果。

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.