Fast Counting in Machine Learning Applications
2018-04-12Code Available0· sign in to hype
Subhadeep Karan, Matthew Eichhorn, Blake Hurlburt, Grant Iraci, Jaroslaw Zola
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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.