Cardinality Estimation of LIKE Predicate Queries using Deep Learning
Summary: Deep-learning cardinality estimation for LIKE predicates via an N-gram table and conditional-regression head. LEADER generates shareable training data across related LIKE queries with a join-order strategy for fast, accurate estimates on real datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Suyong Kwon (Seoul National University)
- 2. Kyuseok Shim (Seoul National University)
- 3. Woohwan Jung (Hanyang University)
BibTeX Citation
@inproceedings{kwon_sigmod25,
title = {{Cardinality Estimation of LIKE Predicate Queries using Deep Learning}},
author = {Kwon, Suyong and Shim, Kyuseok and Jung, Woohwan},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709670},
url = {https://dl.acm.org/doi/10.1145/3709670},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,505 | The Case For Language Model Approximated LIKE Predicate | 2026 | SIGMOD | 5.093636e-05 |
| 10,508 | Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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