@inproceedings{9b1d4df3cf944c1db3c5d37ed3d62603,
title = "Fast counting in machine learning applications",
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.",
author = "S. Karan and M. Eichhorn and B. Hurlburt and G. Iraci and J. Zola",
note = "Publisher Copyright: {\textcopyright} 34th Conference on Uncertainty in Artificial Intelligence 2018. All rights reserved.; 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018 ; Conference date: 06-08-2018 Through 10-08-2018",
year = "2018",
language = "English",
series = "34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018",
publisher = "Association For Uncertainty in Artificial Intelligence (AUAI)",
pages = "540--549",
editor = "Ricardo Silva and Amir Globerson and Amir Globerson",
booktitle = "34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018",
}