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.
Authors
- Vidya Setlur (ORCID: https://orcid.org/0000-0003-3722-406X)
- Dennis Bromley (ORCID: https://orcid.org/0009-0007-0303-8062)
Institutions
- Tableau Software (United States) (US)
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