{"ID":2830808,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.09836","arxiv_id":"2512.09836","title":"Fast Factorized Learning: Powered by In-Memory Database Systems","abstract":"Learning models over factorized joins avoids redundant computations by identifying and pre-computing shared cofactors. Previous work has investigated the performance gain when computing cofactors on traditional disk-based database systems. Due to the absence of published code, the experiments could not be reproduced on in-memory database systems. This work describes the implementation when using cofactors for in-database factorized learning. We benchmark our open-source implementation for learning linear regression on factorized joins with PostgreSQL -- as a disk-based database system -- and HyPer -- as an in-memory engine. The evaluation shows a performance gain of factorized learning on in-memory database systems by 70\\% to non-factorized learning and by a factor of 100 compared to disk-based database systems. Thus, modern database engines can contribute to the machine learning pipeline by pre-computing aggregates prior to data extraction to accelerate training.","short_abstract":"Learning models over factorized joins avoids redundant computations by identifying and pre-computing shared cofactors. Previous work has investigated the performance gain when computing cofactors on traditional disk-based database systems. Due to the absence of published code, the experiments could not be reproduced on...","url_abs":"https://arxiv.org/abs/2512.09836","url_pdf":"https://arxiv.org/pdf/2512.09836v1","authors":"[\"Bernhard Stöckl\",\"Maximilian E. Schüle\"]","published":"2025-12-10T17:14:37Z","proceeding":"cs.DB","tasks":"[\"cs.DB\",\"cs.LG\"]","methods":"[]","has_code":false}