Fast Counting In Machine Learning Applications
Karan Subhadeep, Eichhorn Matthew, Hurlburt Blake, Iraci Grant, Zola Jaroslaw. Arxiv 2018
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.
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