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)
Incoming Non-self Citations Over Time
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}
}Incoming Citations (Sorted by Pagerank)
Showing 37 of 37 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 30 of 30 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 982 | Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation | 2022 | VLDB |
| 2 | 569 | Towards a Robust Query Optimizer: A Principled and Practical Approach | 2005 | SIGMOD |
| 3 | 10,412 | Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis] | 2026 | SIGMOD |
| 4 | 4,457 | Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process | 2022 | SIGMOD |
| 5 | 4,563 | Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts | 2022 | SIGMOD |
| 6 | 6,660 | Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation | 2023 | SIGMOD |
| 7 | 10,294 | Data-Agnostic Cardinality Learning from Imperfect Workloads | 2025 | VLDB |
| 8 | 2,522 | Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation | 2022 | VLDB |
| 9 | 1,064 | Are We Ready For Learned Cardinality Estimation? | 2021 | VLDB |
| 10 | 5,649 | Sample-Efficient Cardinality Estimation Using Geometric Deep Learning | 2024 | VLDB |