Fast Counting in Machine Learning Applications
Machine Learning
2019-01-09 v3 Machine Learning
Abstract
We propose scalable methods to execute counting queries in machine learning applications. To achieve memory and computational efficiency, we abstract counting queries and their context such that the counts can be aggregated as a stream. We demonstrate performance and scalability of the resulting approach on random queries, and through extensive experimentation using Bayesian networks learning and association rule mining. Our methods significantly outperform commonly used ADtrees and hash tables, and are practical alternatives for processing large-scale data.
Cite
@article{arxiv.1804.04640,
title = {Fast Counting in Machine Learning Applications},
author = {Subhadeep Karan and Matthew Eichhorn and Blake Hurlburt and Grant Iraci and Jaroslaw Zola},
journal= {arXiv preprint arXiv:1804.04640},
year = {2019}
}