Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process

Summary: Lightweight, uncertainty-aware cardinality estimation via Neural Network Gaussian Process (NNGP); trained in seconds with calibrated predictions. Bayesian deep learning; NNGP yields a universal nonparametric model (GP limit), robust to workload shift. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6467
Venue: SIGMOD
Year: 2022
Pagerank: 6.1929999e-05
Overall Rank: 4,434 | 69.16%
DOI: 10.1145/3514221.3526156

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,401	ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads	2024	VLDB	5.5285035e-05
6,600	Missing Data Imputation with Uncertainty-Driven Network	2024	SIGMOD	4.9972581e-05
6,714	Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks	2024	SIGMOD	4.9512171e-05
9,485	Spatial Query Optimization With Learning	2024	VLDB	4.3341665e-05
9,878	PRICE: A Pretrained Model for Cross-Database Cardinality Estimation	2025	VLDB	4.2656547e-05
10,032	Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads	2026	SIGMOD	4.1945683e-05
10,203	Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization	2026	SIGMOD	4.1945683e-05
10,590	ACE: A Cardinality Estimator for Set-Valued Queries	2025	VLDB	4.1945683e-05
11,190	Efficient and Effective Cardinality Estimation for Skyline Family	2023	SIGMOD	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 36 of 36 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
64	Improved Histograms for Selectivity Estimation of Range Predicates	1996	SIGMOD	0.00063612837
71	How Good Are Query Optimizers, Really?	2016	VLDB	0.00059038975
102	The Case for Learned Index Structures	2018	SIGMOD	0.00049545203
141	Selectivity Estimation Without the Attribute Value Independence Assumption	1997	VLDB	0.00041786333
183	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036721403
204	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00034784455
217	Ripple Joins for Online Aggregation	1999	SIGMOD	0.00033536712
333	Neo: A Learned Query Optimizer	2019	VLDB	0.00027206884
372	Selectivity Estimation using Probabilistic Models	2001	SIGMOD	0.00025354779
424	Tuning Database Configuration Parameters with iTuned	2009	VLDB	0.00023616398
429	The Aqua Approximate Query Answering System	1999	SIGMOD	0.00023476494
514	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.0002124895
608	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00019235898
758	Deep Unsupervised Cardinality Estimation	2020	VLDB	0.0001706608
806	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.00016434274
826	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00016224841
910	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00015423056
943	Wander Join: Online Aggregation via Random Walks	2016	SIGMOD	0.00015145883
1,254	Selectivity Estimation for Range Predicates using Lightweight Models	2019	VLDB	0.00013027411
1,369	Random Sampling over Joins Revisited	2018	SIGMOD	0.00012339777
1,478	Learning Multi-dimensional Indexes	2020	SIGMOD	0.00011762542
1,547	Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions	2011	VLDB	0.00011442359
1,703	Are We Ready For Learned Cardinality Estimation?	2021	VLDB	0.00010836769
1,855	AI Meets AI: Leveraging Query Executions to Improve Index Recommendations	2019	SIGMOD	0.00010315245
1,874	Knowing When You’re Wrong: Building Fast and Reliable Approximate Query Processing Systems	2014	SIGMOD	0.00010244443
1,902	Black or White? How to Develop an AutoTuner for Memory-based Analytics	2020	SIGMOD	0.00010157713
2,142	Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities	2019	SIGMOD	9.4507296e-05
2,364	Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries	2020	SIGMOD	8.9554751e-05
2,501	DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models	2019	SIGMOD	8.6453446e-05
2,969	Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models	2017	VLDB	7.7974762e-05
3,499	Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation	2021	VLDB	7.0376445e-05
3,580	Query Performance Prediction for Concurrent Queries using Graph Embedding	2020	VLDB	6.9500996e-05
3,778	A Learned Sketch for Subgraph Counting	2021	SIGMOD	6.7747398e-05
3,954	Efficiently Approximating Selectivity Functions using Low Overhead Regression Models	2020	VLDB	6.5926838e-05
4,060	CDFShop: Exploring and Optimizing Learned Index Structures	2020	SIGMOD	6.4836825e-05
4,867	Application Driven Graph Partitioning	2020	SIGMOD	5.8651797e-05

Semantically Similar Papers

Overall Rank	Paper	Year	Venue	Pagerank
1,703	Are We Ready For Learned Cardinality Estimation?	2021	VLDB	0.00010836769
910	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00015423056
758	Deep Unsupervised Cardinality Estimation	2020	VLDB	0.0001706608
7,474	Cardinality Estimation of Approximate Substring Queries using Deep Learning	2022	VLDB	4.7194345e-05
9,726	Cardinality Estimation of LIKE Predicate Queries using Deep Learning	2025	SIGMOD	4.2943379e-05
4,417	Robust Query Driven Cardinality Estimation under Changing Workloads	2023	VLDB	6.2037371e-05
2,364	Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries	2020	SIGMOD	8.9554751e-05
7,221	Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation	2023	SIGMOD	4.797194e-05
3,449	Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation	2022	VLDB	7.0824319e-05
6,383	Sample-Efficient Cardinality Estimation Using Geometric Deep Learning	2024	VLDB	5.0884322e-05