BIGDATA: F: Critical Visualization Technologies for Analyzing and Understanding Big Network Data

大数据给各个研究和实践领域带来了机遇和挑战。可视化已经被证明是一种有效的大数据知识发现和叙事工具。该项目旨在为大网络数据开发新的可视化技术，既能说明实证结果，又能产生新的发现。尽管已经引入了许多网络可视化技术和工具，但可视化大型动态网络以从网络数据中提取关键实体、结构和趋势仍然是一项具有挑战性的任务。现有的大多数网络可视化解决方案都不是为处理动态网络而设计的，并且对于大型网络的交互式探索来说速度太慢。这个项目将仔细研究大网络可视化问题的整体解决方案的组成部分。研究将主要由社会学研究的数据分析需求驱动，如发现多个网络之间的隐藏关联；然而，项目团队还将研究该解决方案在应急管理、生命科学和网络安全等领域的适用性。由此产生的技术有望极大地提高人们探索和理解大型、复杂的动态网络的能力，用于知识发现、关键决策和讲故事。由于数据的爆炸式增长和图作为内部数据结构和数据驱动应用程序中的可视化表示的普遍使用，这项研究工作是及时的。那些必须处理大型、复杂的动态网络数据的工作人员将受益于本研究项目所带来的先进可视化技术。参与该项目的学生将获得较强的跨学科研究技能，以解决现实问题。这项研究强调了为理解包含复杂关系、结构和趋势的大数据提供全面解决方案的重要性。主要研究课题有：(1)大网络数据的可视化描述与挖掘；(2)动态网络数据建模与可视化；(3)实时、流媒体网络数据的可视化监控与分析；(4)利用动态网络数据进行溯源与叙事。本项目将探索并整合新的网络建模、约简和可视化技术，用于分析大型、多元动态图。由此产生的研究创新将增强现有方法，并研究动态网络可视化分析的新方法，并大大提高其在实际应用中的可用性。在理解异构动态大网络数据时，有针对性的应用、应急服务和社会学向项目团队提出了一些最具挑战性的问题。合作领域专家充分致力于参与评估工作，该工作有望产生可用的技术，使答复者能够以新的方式看待数据，并揭示不同实体/事件之间的复杂关系，以便进行关键决策和减灾规划。项目成果将通过年度会议、研讨会、教程以及项目网站向可视化社区传播，该网站将包括项目状态更新、生成的图像、视频和原型软件。

Big data presents both opportunities and challenges to all fields of study and practice. Visualization has been proven effective as a knowledge discovery and storytelling tool for big data. This project aims to develop new visualization technologies for big network data that will both illustrate empirical findings and generate new discoveries. Although many network visualization techniques and tools have been introduced, visualizing large, dynamic networks to extract key entities, structures, and trends from the network data remains a challenging task. Most of the existing network visualization solutions were not designed for handling dynamic networks and are too slow for interactive exploration of large networks. This project will closely examine the integral parts of a holistic solution for the problem of big network visualization. The research study will be largely driven by the data analysis needs of sociological studies such as finding hidden associations between multiple networks; however, the project team will also investigate the solution's applicability in areas such as emergency management, life science and cyber security. The resulting technologies are expected to drastically enhance one's ability to explore and understand large, complex dynamic networks for knowledge discovery, critical decision making, and storytelling. This research effort is timely because of the explosive growth of data and common use of graphs as both the internal data structure and a visual representation in data-driven applications. Those who must deal with large, complex dynamic network data for their work will benefit from the advanced visualization technologies resulted from this research project. Students participating in this project will acquire strong interdisciplinary research skills for real-world problem solving. This research underscores the importance of providing a comprehensive solution to the understanding of big data containing complex relations, structure, and trends. Primary research topics are: (1) Visual depiction and exploration of big network data; (2) Modeling and visualizing dynamic network data; (3) Visual monitoring and analysis of live, streaming network data; and (4) Provenance and storytelling with dynamic network data. This project will explore and integrate new network modeling, reduction, and visualization techniques for analyzing large, multivariate dynamic graphs. The resulting research innovations will both enhance existing methods and investigate new approaches to dynamic network visual analytics and drastically improve their usability for real-world applications. The targeted applications, emergency service and sociology, present the project team with some of the most challenging problems to address in making sense of heterogeneous dynamic big networks data. The collaborating domain experts are fully committed to participating in the evaluation work, which promises to produce usable technologies that will enable respondents to look at the data in new ways and uncover intricate relations among different entities/events for critical decision making and mitigation planning. The project results will be disseminated to the visualization community and beyond through annual conferences, workshops, and tutorials, and also through the project website which will include project status updates and resulting images, videos, and prototype software.