Robust Query Driven Cardinality Estimation under Changing Workloads
Summary: Robustifies query-driven cardinality estimation under workload/data drift using feature masking and join-consistent sampling bitmaps. Enables strong cross-workload and update generalization, improving runtimes while avoiding the catastrophic regressions of standard models. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Parimarjan Negi (Massachusetts Institute of Technology)
- 2. Ziniu Wu (Massachusetts Institute of Technology)
- 3. Andreas Kipf (Massachusetts Institute of Technology)
- 4. Nesime Tatbul (Intel; Massachusetts Institute of Technology)
- 5. Ryan Marcus (Intel; Massachusetts Institute of Technology)
- 6. Sam Madden (Massachusetts Institute of Technology)
- 7. Tim Kraska (Massachusetts Institute of Technology)
- 8. Mohammad Alizadeh (Massachusetts Institute of Technology)
BibTeX Citation
@article{negi_vldb23,
title = {{Robust Query Driven Cardinality Estimation under Changing Workloads}},
author = {Negi, Parimarjan and Wu, Ziniu and Kipf, Andreas and Tatbul, Nesime and Marcus, Ryan and Madden, Sam and Kraska, Tim and Alizadeh, Mohammad},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {6},
pages = {1520--1533},
doi = {10.14778/3583140.3583164},
url = {https://doi.org/10.14778/3583140.3583164},
year = {2023}
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