Dash: A Bimodal Data Exploration Tool for Interactive Text and Visualizations

Integrating textual content, such as titles, annotations, and captions, with visualizations facilitates comprehension and takeaways during data exploration. Yet current tools often lack mechanisms for integrating meaningful long-form prose with visual data. This paper introduces DASH, a bimodal data exploration tool that supports integrating semantic levels into the interactive process of visualization and text-based analysis. DASH operationalizes a modified version of Lundgard et al.’s semantic hierarchy model that catego-rizes data descriptions into four levels ranging from basic encodings to high-level insights. By leveraging this structured semantic level framework and a large language model’s text generation capabilities, DASH enables the creation of data-driven narratives via drag-and-drop user interaction. Through a preliminary user evaluation, we discuss the utility of DASH’s text and chart integration capabilities when participants perform data exploration with the tool.

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Publication Details

Published
2024-10-13
DOI
https://doi.org/10.1109/vis55277.2024.00059
Citations
3
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
1.04
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article

Dash: A Bimodal Data Exploration Tool for Interactive Text and Visualizations

Vidya Setlur, Dennis Bromley
3 citations
Natural Language Processing Techniques
1.04
article

Dash: A Bimodal Data Exploration Tool for Interactive Text and Visualizations

Vidya Setlur, Dennis Bromley
article en
3 citations

Abstract

Integrating textual content, such as titles, annotations, and captions, with visualizations facilitates comprehension and takeaways during data exploration. Yet current tools often lack mechanisms for integrating meaningful long-form prose with visual data. This paper introduces DASH, a bimodal data exploration tool that supports integrating semantic levels into the interactive process of visualization and text-based analysis. DASH operationalizes a modified version of Lundgard et al.’s semantic hierarchy model that catego-rizes data descriptions into four levels ranging from basic encodings to high-level insights. By leveraging this structured semantic level framework and a large language model’s text generation capabilities, DASH enables the creation of data-driven narratives via drag-and-drop user interaction. Through a preliminary user evaluation, we discuss the utility of DASH’s text and chart integration capabilities when participants perform data exploration with the tool.

Tableau Software (United States) (US)
Openalex Percentile: Top 18%
Natural Language Processing Techniques
1.04
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