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We propose scalable methods to execute counting queries in machine learningapplications. To achieve memory and computational efficiency, we abstractcounting queries and their context such that the counts can be aggregated as astream. We demonstrate performance and scalability of the resulting approach onrandom queries, and through extensive experimentation using Bayesian networkslearning and association rule mining. Our methods significantly outperformcommonly used ADtrees and hash tables, and are practical alternatives forprocessing large-scale data.

Fast Counting in Machine Learning Applications