ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation
Summary: ASM harmonizes autoregressive per-table statistics, sampling for join merging, and multidimensional statistics merging to estimate many queries without join-key independence. This yields better plans with similar or lower overhead and wider query coverage than CE. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kyoungmin Kim (Pohang University of Science and Technology)
- 2. Sangoh Lee (Pohang University of Science and Technology)
- 3. Injung Kim (Handong Global University)
- 4. Wook-Shin Han (Pohang University of Science and Technology)
BibTeX Citation
@inproceedings{kim_sigmod24,
title = {{ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation}},
author = {Kim, Kyoungmin and Lee, Sangoh and Kim, Injung and Han, Wook-Shin},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639300},
url = {https://dl.acm.org/doi/10.1145/3639300},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,879 | Spatial Query Optimization With Learning | 2024 | VLDB | 5.2568354e-05 |
| 9,546 | Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement | 2025 | SIGMOD | 5.1604755e-05 |
| 9,670 | Low Rank Learning for Offline Query Optimization | 2025 | SIGMOD | 5.1452097e-05 |
| 9,718 | A Practical Theory of Generalization in Selectivity Learning | 2025 | VLDB | 5.1353964e-05 |
| 10,294 | Data-Agnostic Cardinality Learning from Imperfect Workloads | 2025 | VLDB | 5.0431863e-05 |
| 10,673 | Qualitative Join Discovery in Data Lakes using Examples | 2026 | SIGMOD | 4.9793485e-05 |
| 11,015 | Vodka: Rethink Benchmarking Philosophy in HTAP Systems | 2026 | VLDB | 4.9793485e-05 |
| 11,333 | The Accuracy of Cardinality Estimators: Unraveling the Evaluation Result Conundrum | 2025 | VLDB | 4.9793485e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 35 of 35 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 | 6,660 | Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation | 2023 | SIGMOD |
| 2 | 569 | Towards a Robust Query Optimizer: A Principled and Practical Approach | 2005 | SIGMOD |
| 3 | 7,364 | Weighted Distinct Sampling: Cardinality Estimation for SPJ Queries | 2021 | SIGMOD |
| 4 | 7,566 | Convolution and Cross-Correlation of Count Sketches Enables Fast Cardinality Estimation of Multi-Join Queries | 2024 | SIGMOD |
| 5 | 85 | Learned Cardinalities: Estimating Correlated Joins with Deep Learning | 2019 | CIDR |
| 6 | 2,846 | FactorJoin: A New Cardinality Estimation Framework for Join Queries | 2023 | SIGMOD |
| 7 | 2,433 | Cardinality Estimation Using Sample Views with Quality Assurance | 2007 | SIGMOD |
| 8 | 1,064 | Are We Ready For Learned Cardinality Estimation? | 2021 | VLDB |
| 9 | 2,522 | Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation | 2022 | VLDB |
| 10 | 688 | Cardinality Estimation Done Right: Index-Based Join Sampling | 2017 | CIDR |