  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.
