# Efficient Discovery of Approximate Order Dependencies

arXiv: https://arxiv.org/abs/2101.02174 · HTML: https://mlsanity.com/paper/2101.02174/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2021.

## Built on by (most cited first, 2 of 2)

- [Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs](https://mlsanity.com/paper/2408.05524/) (2024)
- [Non-Invasive Fairness in Learning through the Lens of Data Drift](https://mlsanity.com/paper/2303.17566/) (2023)

## Abstract (arXiv)

Order dependencies (ODs) capture relationships between ordered domains of attributes. Approximate ODs (AODs) capture such relationships even when there exist exceptions in the data. During automated discovery of ODs, validation is the process of verifying whether an OD holds. We present an algorithm for validating approximate ODs with significantly improved runtime performance over existing methods for AODs, and prove that it is correct and has optimal runtime. By replacing the validation step in a leading algorithm for approximate OD discovery with ours, we achieve orders-of-magnitude improvements in performance.