Learning-based Property Estimation with Polynomials
Summary: Learning-based, polynomial-approximation framework for frequency-based property estimation (NDV, entropy, power sums) from samples/histograms. Learns polynomial coefficients to combine ML adaptability with theoretical error guarantees, unifying previously separate statistical and learning-based estimators. (summarized by gpt-5.4-mini on May 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiajun Li
- 2. Runlin Lei
- 3. Sibo Wang
- 4. Zhewei Wei
- 5. Bolin Ding
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,498 | PLM4NDV: Minimizing Data Access for Number of Distinct Values Estimation with Pre-trained Language Models | 2025 | SIGMOD | 4.1945683e-05 |
| 10,534 | AdaNDV: Adaptive Number of Distinct Value Estimation via Learning to Select and Fuse Estimators | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 39 | Statistical Estimators for Relational Algebra Expressions | 1988 | PODS | 0.00074745564 |
| 378 | Towards Estimation Error Guarantees for Distinct Values | 2000 | PODS | 0.0002497492 |
| 1,797 | Effective Use of Block-Level Sampling in Statistics Estimation | 2004 | SIGMOD | 0.00010523169 |
| 2,762 | FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation | 2021 | VLDB | 8.1585394e-05 |
| 3,021 | Adaptive and Big Data Scale Parallel Execution in Oracle | 2013 | VLDB | 7.6991391e-05 |
| 5,401 | ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads | 2024 | VLDB | 5.5285035e-05 |
| 7,610 | Learning to be a Statistician: Learned Estimator for Number of Distinct Values | 2022 | VLDB | 4.6965039e-05 |
| 7,914 | Efficient Approximate Algorithms for Empirical Entropy and Mutual Information | 2021 | SIGMOD | 4.6179608e-05 |
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