Search Results for author: Giorgio Vinciguerra

Found 3 papers, 2 papers with code

Why Are Learned Indexes So Effective?

1 code implementation ICML 2020 Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra

A recent trend in algorithm design consists of augmenting classic data structures with machine learning models, which are better suited to reveal and exploit patterns and trends in the input data so to achieve outstanding practical improvements in space occupancy and time efficiency.

The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds

1 code implementation PVLDB, 13(8) 2020 Paolo Ferragina, Giorgio Vinciguerra

We present the first learned index that supports predecessor, range queries and updates within provably efficient time and space bounds in the worst case.


Superseding traditional indexes by orchestrating learning and geometry

no code implementations1 Mar 2019 Giorgio Vinciguerra, Paolo Ferragina, Michele 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.

Data Structures and Algorithms E.1; E.4; I.2.6

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