Problem
Research questions and friction points this paper is trying to address.
exploratory data analysis
data fact outliers
heterogeneous facts
analytical scopes
outlier detection
FOX: Visual Exploration of Data Fact Outliers
🤖 AI Summary
This work addresses the limitations of existing exploratory data analysis systems, which struggle to effectively detect outliers at the data fact level and suffer from inconsistent metrics due to heterogeneous analytical scopes. To overcome these challenges, we propose FOX, a visual analytics system that first groups data facts into consistent analytical scopes and then integrates distributional and pattern-based features to establish a unified anomaly scoring mechanism. FOX further introduces an interactive paradigm combining a matrix overview with multiple coordinated detail views to facilitate comprehensive exploration. Through case studies on two public datasets and a user study involving twelve participants, we demonstrate that FOX effectively supports the detection, in-depth analysis, and interpretable exploration of fact-level anomalies.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
fact-level outlier
visual analytics
unified outlier scoring
matrix-based visualization
coordinated multiple views
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