Visual analytics (VA) systems have been widely used to facilitate decision-making and analytical reasoning in various application domains. VA involves visual designs, interaction designs, and data mining, which is a systematic and complex paradigm. In this work, we focus on the design of effective visualizations for complex data and analytical tasks, which is a critical step in designing a VA system. This step is challenging because it requires extensive knowledge about domain problems and visualization to design effective encodings. Existing visualization designs published in top venues are valuable resources to inspire designs for problems with similar data structures and tasks. However, those designs are hard to understand, parse, and retrieve due to the lack of specifications. To address this problem, we build KB4VA, a knowledge base of visualization designs in VA systems with comprehensive labels about their analytical tasks and visual encodings. Our labeling scheme is inspired by a workshop study with 12 VA researchers to learn user requirements in understanding and retrieving professional visualization designs in VA systems. The theme extends Vega-Lite specifications for describing advanced and composited visualization designs in a declarative manner, thus facilitating human understanding and automatic indexing. To demonstrate the usefulness of our knowledge base, we present a user study about design inspirations for VA tasks. In summary, our work opens new perspectives for enhancing the accessibility and reusability of professional visualization designs.
翻译:视觉分析系统(VA)被广泛用于促进不同应用领域的决策和分析推理。 VA涉及视觉设计、互动设计和数据挖掘,这是一个系统和复杂的范式。在这项工作中,我们侧重于设计复杂数据和分析任务的有效可视化设计,这是设计VA系统的一个关键步骤。这个步骤具有挑战性,因为它需要广泛了解域问题和可视化设计以设计有效的编码。在顶层公布的现有可视化设计是宝贵的资源,用于激励设计与类似数据结构和任务有关的问题。然而,这些设计由于缺乏规格,难以理解、分析和检索。为了解决这一问题,我们建立了KB4VA,这是VA系统可视化设计设计的知识库,具有分析任务和视觉编码的全面标签。我们的标签计划受到12个VA研究人员的讲习班研究的启发,以学习用户在理解和检索VA系统专业可视化设计方面的需要。主题扩展了VA-Lite规格,用于以解释性方式描述高级和综合可视化设计,从而便利我们当前可视性设计、理解性和自动索引工作的基础。