F-IVM: Learning over Fast-Evolving Relational Data
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Abstract (arXiv)
F-IVM is a system for real-time analytics such as machine learning applications over training datasets defined by queries over fast-evolving relational databases. We will demonstrate F-IVM for three such applications: model selection, Chow-Liu trees, and ridge linear regression.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jun 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2.
3 papers.
- 2021ARM-Net: Adaptive Relation Modeling Network for Structured Datacited by 24
- 2024Powering In-Database Dynamic Model Slicing for Structured Data Analyticscited by 5
- 2024In-Database Data Imputationcited by 3
- 2024Powering In-Database Dynamic Model Slicing for Structured Data Analytics1 May 2024
- 2024In-Database Data Imputation7 Jan 2024
- 2021ARM-Net: Adaptive Relation Modeling Network for Structured Data5 Jul 2021