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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
h305ed9deb67327b8
Venue
SIGMOD
Year
2019
Pagerank
5.1922392e-05
Overall Rank
9,333 | 37.26%
DOI
10.1145/3299869.3300104

Incoming Non-self Citations Over Time

Authors

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
2,460 Query Processing on Tensor Computation Runtimes 2022 VLDB 8.4348335e-05
8,480 Deep Learning: Systems and Responsibility 2021 SIGMOD 5.333141e-05
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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
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046284649
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