LMFAO: An Engine for Batches of Group-By Aggregates
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Abstract (arXiv)
LMFAO is an in-memory optimization and execution engine for large batches of group-by aggregates over joins. Such database workloads capture the data-intensive computation of a variety of data science applications. We demonstrate LMFAO for three popular models: ridge linear regression with batch gradient descent, decision trees with CART, and clustering with Rk-means.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Aug 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1.
2 papers.
- 2024In-Database Data Imputationcited by 3
- 2022Graphical Join: A New Physical Join Algorithm for RDBMSscited by 0
- 2024In-Database Data Imputation7 Jan 2024
- 2022Graphical Join: A New Physical Join Algorithm for RDBMSs21 Jun 2022