AD+Tree: A Compact Adaptation of Dynamic AD-Trees for Efficient Machine Learning on Large Data Sets
This paper introduces the AD+tree, a data structure for quickly counting the number of records that match conjunctive queries in a data set. The structure is useful for machine learning on large data sets. The AD+tree is an adaptation of the Dynamic AD-tree data structure .
We analyze the performance of AD+trees, comparing them to static AD-trees and Dynamic AD-trees. We show AD+trees maintain a very compact cache that enables them to handle queries on massively large data sets very efficiently even under complex, unstructured query patterns.
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