Analyzing Count Min Sketch with Conservative Updates

Title : Count - Min Sketch

Count-Min Sketch

DEFINITION The Count-Min (CM) Sketch is a compact summary data structure capable of representing a high-dimensional vector and answering queries on this vector, in particular point queries and dot product queries, with strong accuracy guarantees. Such queries are at the core of many computations, so the structure can be used in order to answer a variety of other queries, such as frequent items ...

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

Count-Min Sketch [1] 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 in the low-frequency items, such as in textmining related tasks. Several variants of CMS [5] have been proposed to compensate for the high relative error for low-frequ...

Count-Min Tree Sketch: Approximate counting for NLP

The Count-Min Sketch [1] is a widely adopted structure for approximate event counting in large scale processing. In a previous work [7] we improved the original version of the Count-Min-Sketch (CMS) with conservative update using approximate counters [6, 4] instead of linear counters. These structures are computationaly efficient and improve the average relative error (ARE) of a CMS at constant...

Lossy Conservative Update (LCU) Sketch: Succinct Approximate Count Storage

In this paper, we propose a variant of the conservativeupdate Count-Min sketch to further reduce the overestimation error incurred. Inspired by ideas from lossy counting, we divide a stream of items into multiple windows, and decrement certain counts in the sketch at window boundaries. We refer to this approach as a lossy conservative update (LCU). The reduction in overestimation error of count...