# Automating Date Format Detection for Data Visualization

arXiv: https://arxiv.org/abs/2501.05640 · HTML: https://mlsanity.com/paper/2501.05640/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 19 references that are in the map.
- First posted on arXiv 10 Jan 2025.

## Built on by (most cited first, 6 of 6)

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- [Acceptance-Test-Driven Evaluation Protocols for Business-Centric LLM Systems](https://mlsanity.com/paper/2606.02755/) (2026)
- [Anycast Performance in Context](https://mlsanity.com/paper/2606.04298/) (2026)

## Abstract (arXiv)

Data preparation, specifically date parsing, is a significant bottleneck in analytic workflows. To address this, we present two algorithms, one based on minimum entropy and the other on natural language modeling that automatically derive date formats from string data. These algorithms achieve over 90% accuracy on a large corpus of data columns, streamlining the data preparation process within visualization environments. The minimal entropy approach is particularly fast, providing interactive feedback. Our methods simplify date format extraction, making them suitable for integration into data visualization tools and databases.