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PRICE: A Pretrained Model for Cross-Database Cardinality Estimation

Summary: PRICE is a pretrained, self-attention multitable cardinality estimator that transfers across unseen databases using low-level distribution/query features, avoiding costly per-database training. A compact 40MB model supports lightweight finetuning and robust adaptation to updates, scaling, and workload shifts. (summarized by gpt-5.6-luna on Jul 21 2026)

Paper ID
he9df15688334a36f
Venue
VLDB
Year
2025
Pagerank
5.0584922e-05
Overall Rank
10,216 | 31.32%
DOI
10.14778/3712221.3712231

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zeng_vldb25,
        title = {{PRICE: A Pretrained Model for Cross-Database Cardinality Estimation}},
        author = {Zeng, Tianjing and Lan, Junwei and Ma, Jiahong and Wei, Wenqing and Zhu, Rong and Zhou, Yingli and Li, Pengfei and Ding, Bolin and Lian, Defu and Wei, Zhewei and Zhou, Jingren},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {3},
        pages = {637--650},
        doi = {10.14778/3712221.3712231},
        url = {https://doi.org/10.14778/3712221.3712231},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,184 Path-centric Cardinality Estimation for Subgraph Matching 2025 VLDB 5.0651993e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 39 of 39 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1 Access Path Selection in a Relational Database Management System 1979 SIGMOD 0.0023947656
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061066921
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035864347
98 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034106982
103 Selectivity Estimation Without the Attribute Value Independence Assumption 1997 VLDB 0.00033894985
286 Selectivity Estimation using Probabilistic Models 2001 SIGMOD 0.0002211981
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021167555
371 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00019829769
386 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019444411
406 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019045544
512 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017050173
596 Wander Join: Online Aggregation via Random Walks 2016 SIGMOD 0.00015785583
688 Cardinality Estimation Done Right: Index-Based Join Sampling 2017 CIDR 0.00014753664
692 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014741011
701 Independence is Good: Dependency-Based Histogram Synopses for High-Dimensional Data 2001 SIGMOD 0.00014680907
795 Random Sampling over Joins Revisited 2018 SIGMOD 0.00013938779
866 Approximating Multi-Dimensional Aggregate Range Queries Over Real Attributes 2000 SIGMOD 0.00013381261
982 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.00012714044
1,060 Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions 2011 VLDB 0.00012224575
1,064 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012202282
1,433 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010677711
1,508 Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation 2015 SIGMOD 0.00010440205
2,004 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.2065719e-05
2,216 Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models 2017 VLDB 8.8177753e-05
2,342 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.6060437e-05
2,522 Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation 2022 VLDB 8.3477168e-05
2,583 Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation 2021 VLDB 8.2589758e-05
2,846 FactorJoin: A New Cardinality Estimation Framework for Join Queries 2023 SIGMOD 7.9453616e-05
2,885 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 7.9094988e-05
3,052 A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation 2021 SIGMOD 7.7052471e-05
3,210 Efficiently Approximating Selectivity Functions using Low Overhead Regression Models 2020 VLDB 7.5363533e-05
3,327 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.4207879e-05
4,311 ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads 2024 VLDB 6.6727978e-05
4,457 Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process 2022 SIGMOD 6.5913732e-05
4,538 One Model to Rule them All: Towards Zero-Shot Learning for Databases 2022 CIDR 6.553705e-05
5,456 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1239873e-05
5,840 Pre-training Summarization Models of Structured Datasets for Cardinality Estimation 2022 VLDB 5.9737703e-05
5,871 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 5.9639223e-05
6,416 A Unified Transferable Model for ML-Enhanced DBMS 2022 CIDR 5.7920805e-05
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