Learning Linear Regression Models over Factorized Joins

Summary: Learning linear regression on training data defined by arbitrary joins using factorized representations. Proposes F/FDB, F, F/SQL to factorize cofactors, decouple gradient updates from convergence, and exploit join/union commutativity; factorized joins can be exponentially cheaper, delivering up to 1000x speedups over MADlib, StatsModels, and R. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5192
Venue: SIGMOD
Year: 2016
Pagerank: 0.0001693369
Overall Rank: 536 | 96.33%
DOI: 10.1145/2882903.2882939

Incoming Non-self Citations Over Time

Authors

1. Maximilian Schleich (University of Oxford)
2. Dan Olteanu (University of Oxford)
3. Radu Ciucanu (University of Oxford)

BibTeX Citation

@inproceedings{schleich_sigmod16,
        title = {{Learning Linear Regression Models over Factorized Joins}},
        author = {Schleich, Maximilian and Olteanu, Dan and Ciucanu, Radu},
        series = {{SIGMOD} '16},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2882903.2882939},
        url = {https://dl.acm.org/doi/10.1145/2882903.2882939},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 12 of 62 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,176	Clustering with Set Outliers and Applications in Relational Clustering	2026	PODS	5.093636e-05
10,292	SPALM: A Sparsity-Pattern-Adaptive Library for Matrices	2026	SIGMOD	5.093636e-05
10,466	InferF: Declarative Factorization of AI/ML Inferences over Joins	2026	SIGMOD	5.093636e-05
10,557	Database Views as Explanations for Relational Deep Learning	2026	VLDB	5.093636e-05
10,589	Morphing-based Compression for Data-centric ML Pipelines	2026	VLDB	5.093636e-05
10,636	A Lower Bound on Unambiguous Context Free Grammars via Communication Complexity	2025	PODS	5.093636e-05
10,692	GES: High-Performance Graph Processing Engine and Service in Huawei	2025	SIGMOD	5.093636e-05
11,144	Improved Approximation Algorithms for Relational Clustering	2024	PODS	5.093636e-05
11,421	Lightweight Materialization for Fast Dashboards Over Joins	2023	SIGMOD	5.093636e-05
11,529	Lower Bounds for Sparse Oblivious Subspace Embeddings	2022	PODS	5.093636e-05
11,561	Givens QR Decomposition over Relational Databases	2022	SIGMOD	5.093636e-05
11,871	doppioDB 2.0: Hardware Techniques for Improved Integration of Machine Learning into Databases	2019	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
106	The MADlib Analytics Library or MAD Skills, the SQL	2012	VLDB	0.00033539462
358	FAQ: Questions Asked Frequently	2016	PODS	0.00020243592
411	Worst-case Optimal Join Algorithms	2012	PODS	0.00018902089
490	Design and Implementation of the LogicBlox System	2015	SIGMOD	0.000175757
518	Towards a Unified Architecture for in-RDBMS Analytics	2012	SIGMOD	0.00017167492
579	Incremental Knowledge Base Construction Using DeepDive	2015	VLDB	0.00016217563
715	Learning Generalized Linear Models Over Normalized Data	2015	SIGMOD	0.00014655327
835	Scaling Factorization Machines to Relational Data	2013	VLDB	0.00013721583
860	Aggregation and Ordering in Factorised Databases	2013	VLDB	0.00013560445
1,079	Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML	2014	VLDB	0.00012258469
2,392	FDB: A Query Engine for Factorised Relational Databases	2012	VLDB	8.6404947e-05
3,543	A Comparison of Platforms for Implementing and Running Very Large Scale Machine Learning Algorithms	2014	SIGMOD	7.3268101e-05
3,678	Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype?	2015	SIGMOD	7.207554e-05
4,049	Resource Elasticity for Large-Scale Machine Learning	2015	SIGMOD	6.9369379e-05
4,859	Machine Learning for Big Data	2013	SIGMOD	6.4750538e-05

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