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Selectivity Estimation for Queries Containing Predicates over Set-Valued Attributes
Summary: Introduces selectivity estimation for predicates on set-valued attrs via factorization, turning containment into numeric predicates. Proposes ST and ST-hist factorization with Postgres, NeuroCard, and DeepDB, for improved accuracy and efficiency.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6763
- Venue
- SIGMOD
- Year
- 2023
- Pagerank
- 4.2994116e-05
- Overall Rank
- 9,690 | 32.66%
- DOI
-
10.1145/3626755
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 63 |
Improved Histograms for Selectivity Estimation of Range Predicates |
1996 |
SIGMOD |
0.00063595699 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 92 |
Practical Selectivity Estimation through Adaptive Sampling |
1990 |
SIGMOD |
0.00051431888 |
| 141 |
Selectivity Estimation Without the Attribute Value Independence Assumption |
1997 |
VLDB |
0.00041819767 |
| 181 |
LEO - DB2's LEarning Optimizer |
2001 |
VLDB |
0.00036970794 |
| 203 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034868567 |
| 373 |
Selectivity Estimation using Probabilistic Models |
2001 |
SIGMOD |
0.00025354685 |
| 527 |
Self-tuning Histograms: Building Histograms Without Looking at Data |
1999 |
SIGMOD |
0.00020862475 |
| 606 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019251186 |
| 627 |
Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors |
2009 |
VLDB |
0.00018959896 |
| 752 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.00017138049 |
| 803 |
On the Relative Cost of Sampling for Join Selectivity Estimation |
1994 |
PODS |
0.00016438972 |
| 838 |
Independence is Good: Dependency-Based Histogram Synopses for High-Dimensional Data |
2001 |
SIGMOD |
0.00016024923 |
| 905 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423174 |
| 1,638 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011050093 |
| 1,727 |
QuickSel: Quick Selectivity Learning with Mixture Models |
2020 |
SIGMOD |
0.00010731889 |
| 2,136 |
SASH: A Self-Adaptive Histogram Set for Dynamically Changing Workloads |
2003 |
VLDB |
9.4668797e-05 |
| 2,143 |
Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities |
2019 |
SIGMOD |
9.4437798e-05 |
| 2,167 |
Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation |
2015 |
SIGMOD |
9.3879598e-05 |
| 2,364 |
Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries |
2020 |
SIGMOD |
8.955077e-05 |
| 2,769 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
8.1512848e-05 |
| 2,779 |
Hashed Samples: Selectivity Estimators For Set Similarity Selection Queries |
2008 |
VLDB |
8.1314377e-05 |
| 2,971 |
Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models |
2017 |
VLDB |
7.7935535e-05 |
| 3,924 |
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation |
2021 |
SIGMOD |
6.6227223e-05 |
| 3,955 |
Efficiently Approximating Selectivity Functions using Low Overhead Regression Models |
2020 |
VLDB |
6.5895015e-05 |
| 4,543 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
6.0953507e-05 |
| 6,365 |
Pre-training Summarization Models of Structured Datasets for Cardinality Estimation |
2022 |
VLDB |
5.0892829e-05 |
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