# DataSite: Proactive Visual Data Exploration with Computation of Insight-based Recommendations

arXiv: https://arxiv.org/abs/1802.08621 · HTML: https://mlsanity.com/paper/1802.08621/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2018.

## Built on by (most cited first, 12 of 12)

- [Chart-to-Text: A Large-Scale Benchmark for Chart Summarization](https://mlsanity.com/paper/2203.06486/) (2022)
- [VisText: A Benchmark for Semantically Rich Chart Captioning](https://mlsanity.com/paper/2307.05356/) (2023)
- [ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules](https://mlsanity.com/paper/2304.02173/) (2023)
- [ProactiveVA: Proactive Visual Analytics with LLM-Based UI Agent](https://mlsanity.com/paper/2507.18165/) (2025)
- [Agentic Visualization: Extracting Agent-based Design Patterns from Visualization Systems](https://mlsanity.com/paper/2505.19101/) (2025)
- [Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directions](https://mlsanity.com/paper/2409.19747/) (2024)
- [Personalized Visualization Recommendation](https://mlsanity.com/paper/2102.06343/) (2021)
- [GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation](https://mlsanity.com/paper/2308.13588/) (2023)
- [FactFlow: Automatic Fact Sheet Generation and Customization from Tabular Dataset via AI Chain Design & Implementation](https://mlsanity.com/paper/2502.17909/) (2025)
- [Insight-centric Visualization Recommendation](https://mlsanity.com/paper/2103.11297/) (2021)
- [Context-Mediated Domain Adaptation in Multi-Agent Sensemaking Systems](https://mlsanity.com/paper/2603.24858/) (2026)
- [From Question-First to Analyst-First: Domain-Expert Skills and Verified Knowledge Compilation for Proactive Enterprise Analytics](https://mlsanity.com/paper/2608.28594/) (2026)

## Abstract (arXiv)

Effective data analysis ideally requires the analyst to have high expertise as well as high knowledge of the data. Even with such familiarity, manually pursuing all potential hypotheses and exploring all possible views is impractical. We present DataSite, a proactive visual analytics system where the burden of selecting and executing appropriate computations is shared by an automatic server-side computation engine. Salient features identified by these automatic background processes are surfaced as notifications in a feed timeline. DataSite effectively turns data analysis into a conversation between analyst and computer, thereby reducing the cognitive load and domain knowledge requirements. We validate the system with a user study comparing it to a recent visualization recommendation system, yielding significant improvement, particularly for complex analyses that existing analytics systems do not support well.