# PI2: End-to-end Interactive Visualization Interface Generation from Queries

arXiv: https://arxiv.org/abs/2107.08203 · HTML: https://mlsanity.com/paper/2107.08203/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2021.

## Built on by (most cited first, 10 of 10)

- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [Vi(E)va LLM! A Conceptual Stack for Evaluating and Interpreting Generative AI-based Visualizations](https://mlsanity.com/paper/2402.02167/) (2024)
- [NL2INTERFACE: Interactive Visualization Interface Generation from Natural Language Queries](https://mlsanity.com/paper/2209.08834/) (2022)
- [DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning](https://mlsanity.com/paper/2508.01700/) (2025)
- [Agnostic Visual Recommendation Systems: Open Challenges and Future Directions](https://mlsanity.com/paper/2302.00569/) (2023)
- [V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestions](https://mlsanity.com/paper/2406.15259/) (2024)
- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)
- [MultiVis-Agent: A Multi-Agent Framework with Logic Rules for Reliable and Comprehensive Cross-Modal Data Visualization](https://mlsanity.com/paper/2601.18320/) (2026)
- [Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities](https://mlsanity.com/paper/2603.05542/) (2026)
- [Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?](https://mlsanity.com/paper/2609.26208/) (2026)

## 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.