  The Count-Min Sketch is a widely adopted structure for approximate event
counting in large scale processing. In a previous work we improved the original
version of the Count-Min-Sketch (CMS) with conservative update using
approximate counters instead of linear counters. These structures are
computationaly efficient and improve the average relative error (ARE) of a CMS
at constant memory footprint. These improvements are well suited for NLP tasks,
in which one is interested by the low-frequency items. However, if Log counters
allow to improve ARE, they produce a residual error due to the approximation.
In this paper, we propose the Count-Min Tree Sketch (Copyright 2016 eXenSa. All
rights reserved) variant with pyramidal counters, which are focused toward
taking advantage of the Zipfian distribution of text data.
