Structure-Aware Machine Learning over Multi-Relational Databases
Summary: Structure-aware learning unifies query and model training over multi-relational databases, removing the feature-extraction/export loop and enabling end-to-end optimization. Leverages data and query structure for end-to-end guarantees and speedups via the LMFAO in-memory engine and experiments. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
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| 11,220 | Lightweight Materialization for Fast Dashboards Over Joins | 2023 | SIGMOD | 4.1945683e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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