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)

Paper ID: 6339
Venue: SIGMOD
Year: 2021
Pagerank: 5.1879626e-05
Overall Rank: 9,962 | 31.66%
DOI: 10.1145/3448016.3461670

Incoming Non-self Citations Over Time

Authors

1. Maximilian Schleich (University of Washington)

BibTeX Citation

@inproceedings{schleich_sigmod21,
        title = {{Structure-Aware Machine Learning over Multi-Relational Databases}},
        author = {Schleich, Maximilian},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3461670},
        url = {https://dl.acm.org/doi/10.1145/3448016.3461670},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,421	Lightweight Materialization for Fast Dashboards Over Joins	2023	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank

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