Flow-Loss: Learning Cardinality Estimates That Matter
Summary: Flow-Loss trains cardinality estimators against optimizer plan costs via a flow-routing formulation over plan graphs, rather than average Q-Error. On the 16K-query CEB benchmark, it yields better runtimes and markedly stronger generalization to unseen templates despite worse estimation accuracy. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Parimarjan Negi (Massachusetts Institute of Technology)
- 2. Ryan Marcus (Intel; Massachusetts Institute of Technology)
- 3. Andreas Kipf (Massachusetts Institute of Technology)
- 4. Hongzi Mao (Massachusetts Institute of Technology)
- 5. Nesime Tatbul (Intel; Massachusetts Institute of Technology)
- 6. Tim Kraska (Massachusetts Institute of Technology)
- 7. Mohammad Alizadeh (Massachusetts Institute of Technology)
BibTeX Citation
@article{negi_vldb21,
title = {{Flow-Loss: Learning Cardinality Estimates That Matter}},
author = {Negi, Parimarjan and Marcus, Ryan and Kipf, Andreas and Mao, Hongzi and Tatbul, Nesime and Kraska, Tim and Alizadeh, Mohammad},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {11},
pages = {2019--2032},
doi = {10.14778/3476249.3476259},
url = {https://doi.org/10.14778/3476249.3476259},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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