Accelerating String-Key Learned Index Structures via Memoization-based Incremental Training

arXiv:2403.11472v1 Announce Type: new
Abstract: Learned indexes use machine learning models to learn the mappings between keys and their corresponding positions in key-value indexes. These indexes use the mapping information as training data. Learned indexes require frequent retrainings of their models to incorporate the changes introduced by update queries. To efficiently retrain the models, existing learned index systems often harness a linear algebraic QR factorization technique that performs matrix decomposition. This factorization approach processes all key-position pairs during each retraining, …

abstract arxiv cs.ar cs.db cs.lg data incremental index information key keys learn machine machine learning machine learning models mapping memoization queries string training training data type update value via