AI 中文总结
针对现有EDA系统在数据事实异常值分析上的不足，提出可视化分析系统FOX，通过分组数据事实、计算统一异常值分数及多面板关联视图，经评估可有效检测分析数据事实异常值。
AI 中文摘要
探索性数据分析（EDA）系统会提取并呈现数据事实，以总结趋势、相关性等有意义的模式，从而实现高效的数据集探索。然而，现有方法很少在数据事实层面考虑异常值检测，且来自不同分析范围的异构事实常被聚合到单一视图中，导致难以定义有意义的指标并有效分析数据事实异常值。为填补这一空白，我们提出FOX，一款用于交互式数据事实异常值探索的新型可视化分析系统。FOX将数据事实组织为具有一致分析范围的组，并计算结合了基于分布和基于模式组件的统一异常值分数。其界面包含用于数据准备的上传面板，以及两个协调的探索面板：概览面板采用基于矩阵的可视化，可直观呈现所有数据事实的概览；主面板提供四个关联视图，用于集群级和事实级分析。我们通过两个针对公开数据集的使用场景，以及对12名参与者的深度访谈，评估了该系统的可用性和有效性。结果表明，FOX能够实现数据事实异常值的有意义检测、分析与解释。
英文摘要
Exploratory Data Analysis (EDA) systems extract and present data facts to summarize meaningful patterns such as trends and correlations for efficient dataset exploration. However, existing approaches rarely consider outlier detection at the level of data facts,and heterogeneous facts from different analytical scopes are often aggregated in a single view, making it difficult to define meaningful metrics and effectively analyze data fact outliers. To fill this gap, we present FOX, a novel visual analytics system for interactive data Fact Outlier eXploration. FOX organizes data facts into groups with consistent analytical scopes and computes a unified outlier score that combines distribution-based and pattern-based components. Its interface comprises an Upload Panel for data preparation and two coordinated exploration panels: the Overview Panel employs a matrix-based visualization to enable an intuitive overview of all data facts, and the Main Panel provides four linked views for cluster-level and fact-level analysis. We evaluated the usability and effectiveness of the system through two usage scenarios on public datasets and in-depth interviews with 12 participants. The results show that FOX enables meaningful detection, analysis, and explanation of data fact outliers.
