Superseding traditional indexes by orchestrating learning and geometry

1 Mar 2019Giorgio VinciguerraPaolo FerraginaMichele Miccinesi

We design the first learned index that solves the dictionary problem with time and space complexity provably better than classic data structures for hierarchical memories, such as B-trees, and modern learned indexes. We call our solution the Piecewise Geometric Model index (PGM-index) because it turns the indexing of a sequence of keys into the coverage of a sequence of 2D-points via linear models (i.e. segments) suitably learned to trade query time vs space efficiency... (read more)

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