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Machine Learning, Linear Algebra, and More: Is SQL All You Need?

Summary: Systematic translation of procedural algorithms (machine learning, linear algebra, and other intensive computations) into pure SQL, enabling complex in-DB declarative implementations. Shows modern engines (e.g., HyPer) can match or outperform native linear-algebra libraries. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
443
Venue
CIDR
Year
2022
Pagerank
5.8240599e-05
Overall Rank
6,604 | 54.70%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{blacher_cidr22,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '22},
        title = {{Machine Learning, Linear Algebra, and More: Is SQL All You Need?}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Blacher, Mark and Giesen, Joachim and Laue, Sören and Klaus, Julien and Leis, Viktor},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank Cited Paper Year Venue Pagerank
155 MAD Skills: New Analysis Practices for Big Data 2009 VLDB 0.00028713176
894 Froid: Optimization of Imperative Programs in a Relational Database 2018 VLDB 0.00013367658
3,257 One WITH RECURSIVE is Worth Many GOTOs 2021 SIGMOD 7.590651e-05
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