Frequency Estimation in Data Streams: Learning the Optimal Hashing Scheme

TL;DR

This paper introduces a machine learning-based method for frequency estimation in data streams that optimizes hashing schemes using observed data, significantly improving accuracy over existing algorithms.

Contribution

It proposes a novel optimization and machine learning framework for hashing in data streams, enabling near-optimal frequency distribution compression and improved estimation accuracy.

Findings

Outperforms existing methods by 1-2 orders of magnitude in estimation error

Achieves 45-90% reduction in expected estimation error

Develops an efficient algorithm with linear-time exact solutions in certain cases

Abstract

We present a novel approach for the problem of frequency estimation in data streams that is based on optimization and machine learning. Contrary to state-of-the-art streaming frequency estimation algorithms, which heavily rely on random hashing to maintain the frequency distribution of the data steam using limited storage, the proposed approach exploits an observed stream prefix to near-optimally hash elements and compress the target frequency distribution. We develop an exact mixed-integer linear optimization formulation, which enables us to compute optimal or near-optimal hashing schemes for elements seen in the observed stream prefix; then, we use machine learning to hash unseen elements. Further, we develop an efficient block coordinate descent algorithm, which, as we empirically show, produces high quality solutions, and, in a special case, we are able to solve the proposed formulation exactly in linear time using dynamic programming. We empirically evaluate the proposed approach both on synthetic datasets and on real-world search query data. We show that the proposed approach outperforms existing approaches by one to two orders of magnitude in terms of its average (per element) estimation error and by 45-90% in terms of its expected magnitude of estimation error.