CAREER: Design Decision Patterns for Visualizing Multivariate Graphs

多变量图，或将与多个不同变量相关联的实体链接在一起的数据集，出现在广泛的问题中。例如，数据集可以是包括社会经济统计数据的地理空间位置，通过公共交通系统连接在一起。众所周知，这些多变量图形很难可视化，因为数据变量的数量超过了可用视觉线索的数量--这些线索包括颜色、大小、位置等。该项目的目标是建立一套经过验证的、可推广的技术，用于可视化多变量图形并与其交互。三个目标应用领域将推动研究：一个是癌症生物学，第二个是城市交通，第三个是粒子物理。选择这些领域是为了代表多变量图发挥核心作用的广泛可能的应用，从而促进可推广的结果。这项研究的多学科性质，以及与我们目标应用领域的领域专家的密切合作，将为本科生和研究生提供一个独特的教育环境，同时也扩大了对计算机科学的参与，超越了传统的边界。这是第一次系统地、以问题为导向的努力，考虑使用不同的应用领域来实现多变量图形的可视化，目标是开发一套可推广的技术和原则，以支持广泛的可视化和数据分析任务。这项研究将与领域专家一起使用设计研究方法进行，这是一种深度协作和以用户为中心的可视化研究方法。这项工作的主要影响将是验证可视化设计决策模式，以实现有效的视觉表示和用户驱动的复杂多变量图形的探索，从而为可视化这一日益重要的数据类型提供更全面的技术基础。由此产生的设计决策模式将支持我们在目标应用程序领域中的持续研究和发现，并概括为广泛的现实世界问题类别。此外，这些模式将成为可视化多变量图表的软件工具的基础，通过考虑其中嵌入的各种关系，这些工具有效地支持对这些复杂数据类型的探索和理解。结果和软件将向我们目标应用领域的研究社区传播，但也将通过项目网站http://mvgraphs.sci.utah.edu.进行更广泛的传播

Multivariate graphs, or datasets that link together entities that are associated with multiple different variables, occur in a broad range of problems. For example, the dataset could be geospatial locations that include socio-economic statistics, linked together through a public transportation system. These multivariate graphs are notoriously difficult to visualize because the number of data variables exceeds the number of available visual cues - these cues include color, size, position, etc. The goal of this project is to establish a set of validated and generalizable techniques for visualizing and interacting with multivariate graphs. Three target application areas will drive the investigations: one in cancer biology, a second in urban transportation, and a third in particle physics. These areas were chosen to represent a wide spectrum of possible applications in which multivariate graphs play a central role, thus fostering generalizable results. The multidisciplinary nature of the research and the close collaboration with domain experts in our target application areas will provide a unique educational environment for undergraduate and graduate students, while also broadening the participation in computer science beyond traditional boundaries.This is the first systematic, problem-driven effort to consider the visualization of multivariate graphs using a diverse set of application areas, with the goal of developing a generalizable set of techniques and principles for supporting a broad range of visualization and data analysis tasks. The research will be conducted with domain experts using a design study methodology, which is a deeply collaborative and user-centered approach to visualization research. The primary impact of this work will be validated visualization design decision patterns for effective visual representation and user-driven exploration of complex multivariate graphs, resulting in a more comprehensive foundation of techniques for visualizing this increasingly important data type. The resulting design decision patterns will support ongoing research and discovery in our target application areas, as well generalize to a broad class of real-world problems. Furthermore, these patterns will form the foundation of software tools for visualizing multivariate graphs that effectively support exploration and sense-making of these complex data types by taking into account the varied relationships embedded within. Results and software will be disseminated to both the research communities of our target application areas, but also more broadly through the project website at http://mvgraphs.sci.utah.edu.