HAXplorer: Interactive visual exploration of hierarchical item and attribute spaces

Analyzing tabular data by leveraging hierarchical structures for its items and attributes is a promising approach to scale for dataset sizes that make per-item and per-attribute analysis impractical. Existing approaches face limitations in supporting both item and attribute hierarchies, enabling use...

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Veröffentlicht in:Computers & graphics Jg. 129; S. 104233
Hauptverfasser: Blum, Michael, Blum, Jonas, Sachdeva, Madhav, Bernard, Jürgen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.06.2025
Schlagworte:
Data exploration
Hierarchical aggregation
Interactive visual data analysis
Tabular data
Visual analytics
Data exploration
Hierarchical aggregation
Visual analytics
Interactive visual data analysis
Tabular data
ISSN:0097-8493
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Zusammenfassung:Analyzing tabular data by leveraging hierarchical structures for its items and attributes is a promising approach to scale for dataset sizes that make per-item and per-attribute analysis impractical. Existing approaches face limitations in supporting both item and attribute hierarchies, enabling user-controlled hierarchy creation, and ensuring visual scalability and interaction utility. We present HAXplorer, a visual analytics approach that enables users to create both item and attribute hierarchies, and to explore the resulting tabular data space by leveraging item and attribute aggregates. We demonstrate the generalizability of HAXplorer through usage scenarios across three diverse domains and evaluate its usefulness in a task-based user study. Usability is assessed through a perceived readability questionnaire and qualitative feedback. In addition to introducing a novel visual analytics system, our work offers insights into visual literacy, design validation methodologies, the positioning of HAXplorer within the broader landscape of biclustering techniques, and highlights the generative power of abstraction. [Display omitted] •Creation and exploration of hierarchical structures for both items and attributes are fully supported.•Semantic and statistical criteria can be used to dynamically construct hierarchies.•Five coordinated views enable item-based, attribute-based, and crosscutting analyses.•Hierarchy-driven aggregation maintains clarity and usability for complex data spaces.•Validation and usage scenarios show utility across diverse application domains.
ISSN:0097-8493
DOI:10.1016/j.cag.2025.104233