Draco has been developed as an automated visualization recommendation system formalizing design knowledge as logical constraints in ASP (Answer-Set Programming). With an increasing set of constraints and incorporated design knowledge, even visualization experts lose overview in Draco and struggle to retrace the automated recommendation decisions made by the system. Our paper proposes an Visual Analytics (VA) approach to visualize and analyze Draco's constraints. Our VA approach is supposed to enable visualization experts to accomplish identified tasks regarding the knowledge base and support them in better understanding Draco. We extend the existing data extraction strategy of Draco with a data processing architecture capable of extracting features of interest from the knowledge base. A revised version of the ASP grammar provides the basis for this data processing strategy. The resulting incorporated and shared features of the constraints are then visualized using a hypergraph structure inside the radial-arranged constraints of the elaborated visualization. The hierarchical categories of the constraints are indicated by arcs surrounding the constraints. Our approach is supposed to enable visualization experts to interactively explore the design rules' violations based on highlighting respective constraints or recommendations. A qualitative and quantitative evaluation of the prototype confirms the prototype's effectiveness and value in acquiring insights into Draco's recommendation process and design constraints.

翻译：Draco 被开发为一种自动可视化推荐系统，它将设计知识形式化为 ASP（回答集编程）中的逻辑约束。随着约束集合及纳入的设计知识日益增多，即使可视化专家也难以全面掌握 Draco 的情况，并难以追溯系统做出的自动推荐决策。本文提出了一种可视分析（VA）方法，用于可视化和分析 Draco 的约束。我们的 VA 方法旨在使可视化专家能够完成与知识库相关的已识别任务，并支持他们更好地理解 Draco。我们扩展了 Draco 现有的数据提取策略，引入了一种能够从知识库中提取感兴趣特征的数据处理架构。ASP 语法的修订版本构成了此数据处理策略的基础。随后，利用所设计可视化中径向排列的约束内部的超图结构，对由此产生的约束的合并与共享特征进行可视化。约束的层次类别由环绕约束的弧线表示。我们的方法旨在使可视化专家能够基于高亮相关约束或推荐，交互式地探索设计规则的违反情况。对原型的定性和定量评估证实了其在洞察 Draco 的推荐过程和设计约束方面的有效性和价值。