Deep Query Optimization

Summary: Introduces deep learning models for cost estimation and index selection in distributed query optimization. Early selectivity estimation results reach 97% accuracy, showing DL can learn complex cost functions for planning. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5697
Venue: SIGMOD
Year: 2019
Pagerank: 5.3055088e-05
Overall Rank: 9,215 | 36.78%
DOI: 10.1145/3299869.3300104

Incoming Non-self Citations Over Time

Authors

1. Tin Vu (University of California Riverside)

BibTeX Citation

@inproceedings{vu_sigmod19,
        title = {{Deep Query Optimization}},
        author = {Vu, Tin},
        series = {{SIGMOD} '19},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3299869.3300104},
        url = {https://dl.acm.org/doi/10.1145/3299869.3300104},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,823	Query Processing on Tensor Computation Runtimes	2022	VLDB	8.0893814e-05
8,328	Deep Learning: Systems and Responsibility	2021	SIGMOD	5.4535681e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
43	The Case for Learned Index Structures	2018	SIGMOD	0.00046060254

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