PI2: End-to-end Interactive Visualization Interface Generation from Queries
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Abstract (arXiv)
Interactive visual analysis interfaces are critical in nearly every data task. However, creating new interfaces is deeply challenging, as it requires the developer to understand the queries needed to express the desired analysis task, design the appropriate interface to express those queries for the task, and implement the interface using a combination of visualization, browser, server, and database technologies. Although prior work generates a set of interactive widgets that can express an input query log, this paper presents PI2, the first system to generate fully functional visual analysis interfaces from an example sequence of analysis queries. PI2 analyzes queries syntactically and represents a set of queries using a novel Difftree structure that encodes systematic variations between query abstract syntax trees. PI2 then maps each Difftree to a visualization that renders its results, the variations in each Difftree to interactions, and generates a good layout for the interface. We show that PI2 can express data-oriented interactions in existing visualization interaction taxonomies, reproduce or improve several real-world visual analysis interfaces, generate interfaces in 2-19s (median 6s), and scale linearly with the number of queries.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jul 2021.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 1; 2026: 3.
10 papers.
- 2024HAIChart: Human and AI Paired Visualization Systemcited by 26
- 2024Vi(E)va LLM! A Conceptual Stack for Evaluating and Interpreting Generative AI-based Visualizationscited by 10
- 2022NL2INTERFACE: Interactive Visualization Interface Generation from Natural Language Queriescited by 8
- 2025DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoningcited by 8
- 2023Agnostic Visual Recommendation Systems: Open Challenges and Future Directionscited by 4
- 2023SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebookscited by 3
- 2024V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestionscited by 3
- 2026MultiVis-Agent: A Multi-Agent Framework with Logic Rules for Reliable and Comprehensive Cross-Modal Data Visualizationcited by 3
- 2026Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunitiescited by 0
- 2026Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?cited by 0
- 2026Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?11 Aug 2026
- 2026Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities4 Mar 2026
- 2026MultiVis-Agent: A Multi-Agent Framework with Logic Rules for Reliable and Comprehensive Cross-Modal Data Visualization26 Jan 2026
- 2025DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning3 Aug 2025
- 2024V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestions21 Jun 2024
- 2024HAIChart: Human and AI Paired Visualization System16 Jun 2024
- 2024Vi(E)va LLM! A Conceptual Stack for Evaluating and Interpreting Generative AI-based Visualizations3 Feb 2024
- 2023SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks4 May 2023
- 2023Agnostic Visual Recommendation Systems: Open Challenges and Future Directions1 Feb 2023
- 2022NL2INTERFACE: Interactive Visualization Interface Generation from Natural Language Queries19 Sep 2022