# Extracting Syntactic Patterns from Databases

arXiv: https://arxiv.org/abs/1710.11528 · HTML: https://mlsanity.com/paper/1710.11528/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2017.

## Built on by (most cited first, 3 of 3)

- [Auto-Validate: Unsupervised Data Validation Using Data-Domain Patterns Inferred from Data Lakes](https://mlsanity.com/paper/2104.04659/) (2021)
- [Auto-Tag: Tagging-Data-By-Example in Data Lakes](https://mlsanity.com/paper/2112.06049/) (2021)
- [Auto-Test: Learning Semantic-Domain Constraints for Unsupervised Error Detection in Tables](https://mlsanity.com/paper/2504.10762/) (2025)

## Abstract (arXiv)

Many database columns contain string or numerical data that conforms to a pattern, such as phone numbers, dates, addresses, product identifiers, and employee ids. These patterns are useful in a number of data processing applications, including understanding what a specific field represents when field names are ambiguous, identifying outlier values, and finding similar fields across data sets. One way to express such patterns would be to learn regular expressions for each field in the database. Unfortunately, exist- ing techniques on regular expression learning are slow, taking hundreds of seconds for columns of just a few thousand values. In contrast, we develop XSystem, an efficient method to learn patterns over database columns in significantly less time. We show that these patterns can not only be built quickly, but are expressive enough to capture a number of key applications, including detecting outliers, measuring column similarity, and assigning semantic labels to columns (based on a library of regular expressions). We evaluate these applications with datasets that range from chemical databases (based on a collaboration with a pharmaceutical company), our university data warehouse, and open data from MassData.gov.