In-Database Learning with Sparse Tensors
Summary: Unified relational/tensor framework for in-database training and evaluation of ridge, polynomial, factorization-machine, and PCA models. Exploits schema/query structure and tensor algorithms to eliminate export loops, delivering deployed retail-planning speedups. (summarized by gpt-5.6-luna on Jul 26 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Mahmoud Abo Khamis (RelationalAI, Inc)
- 2. Hung Q. Ngo (RelationalAI, Inc)
- 3. XuanLong Nguyen (University of Michigan)
- 4. Dan Olteanu (University of Oxford)
- 5. Maximilian Schleich (University of Oxford)
BibTeX Citation
@inproceedings{khamis_pods18,
address = {New York, NY, USA},
series = {{PODS} '18},
title = {{In-Database Learning with Sparse Tensors}},
url = {https://dl.acm.org/doi/10.1145/3196959.3196960},
doi = {10.1145/3196959.3196960},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Khamis, Mahmoud Abo and Ngo, Hung Q. and Nguyen, XuanLong and Olteanu, Dan and Schleich, Maximilian},
year = {2018}
}
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