# Count-Min-Log sketch: Approximately counting with approximate counters

arXiv: https://arxiv.org/abs/1502.04885 · HTML: https://mlsanity.com/paper/1502.04885/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2015.

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

- [Conformal Frequency Estimation using Discrete Sketched Data with Coverage for Distinct Queries](https://mlsanity.com/paper/2211.04612/) (2022)
- [CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models](https://mlsanity.com/paper/2312.03256/) (2023)
- [Conformal Frequency Estimation with Sketched Data](https://mlsanity.com/paper/2204.04270/) (2022)
- [A Bayesian nonparametric approach to count-min sketch under power-law data streams](https://mlsanity.com/paper/2102.03743/) (2021)
- [Learning-augmented count-min sketches via Bayesian nonparametrics](https://mlsanity.com/paper/2102.04462/) (2021)
- [A Scalable Space-efficient In-database Interpretability Framework for Embedding-based Semantic SQL Queries](https://mlsanity.com/paper/2302.12178/) (2023)

## Abstract (arXiv)

Count-Min Sketch is a widely adopted algorithm for approximate event counting in large scale processing. However, the original version of the Count-Min-Sketch (CMS) suffers of some deficiences, especially if one is interested by the low-frequency items, such as in text-mining related tasks. Several variants of CMS have been proposed to compensate for the high relative error for low-frequency events, but the proposed solutions tend to correct the errors instead of preventing them. In this paper, we propose the Count-Min-Log sketch, which uses logarithm-based, approximate counters instead of linear counters to improve the average relative error of CMS at constant memory footprint.