  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.
