FOX: Visual Exploration of Data Fact Outliers
Presentation
- Session
- I don't trust you, explain yourself!
- Time
- Friday, Nov 13, 08:36 – 08:48 (US/Eastern) · session 08:00 – 09:30
- Location
- Hall Essex north
Links
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Keywords
Data fact outlier, visual analytics, exploratory data analysis
Abstract
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.
For Practitioners
Data analysts, data scientists, and visualization practitioners may use the ideas in this paper to identify and explore unusual patterns among comparable data facts. FOX demonstrates how fact grouping, outlier scoring, coordinated visualizations, and textual summaries can support more structured exploratory analysis of multidimensional datasets.