  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.
