A Bayesian nonparametric approach to count-min sketch under power-law data streams

by   Emanuele Dolera, et al.

The count-min sketch (CMS) is a randomized data structure that provides estimates of tokens' frequencies in a large data stream using a compressed representation of the data by random hashing. In this paper, we rely on a recent Bayesian nonparametric (BNP) view on the CMS to develop a novel learning-augmented CMS under power-law data streams. We assume that tokens in the stream are drawn from an unknown discrete distribution, which is endowed with a normalized inverse Gaussian process (NIGP) prior. Then, using distributional properties of the NIGP, we compute the posterior distribution of a token's frequency in the stream, given the hashed data, and in turn corresponding BNP estimates. Applications to synthetic and real data show that our approach achieves a remarkable performance in the estimation of low-frequency tokens. This is known to be a desirable feature in the context of natural language processing, where it is indeed common in the context of the power-law behaviour of the data.



There are no comments yet.


page 1

page 2

page 3

page 4


Learning-augmented count-min sketches via Bayesian nonparametrics

The count-min sketch (CMS) is a time and memory efficient randomized dat...

Double-Hashing Algorithm for Frequency Estimation in Data Streams

Frequency estimation of elements is an important task for summarizing da...

Types, Tokens, and Hapaxes: A New Heap's Law

Heap's Law states that in a large enough text corpus, the number of type...

A new Frequency Estimation Sketch for Data Streams

In data stream applications, one of the critical issues is to estimate t...

A Formal Analysis of the Count-Min Sketch with Conservative Updates

Count-Min Sketch with Conservative Updates (CMS-CU) is a popular algorit...

Finding Heavily-Weighted Features in Data Streams

We introduce a new sub-linear space data structure---the Weight-Median S...

Conformalized Frequency Estimation from Sketched Data

A flexible conformal inference method is developed to construct confiden...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.