Sub-optimal Join Order Identification with L1-error
Summary: Introduces L1-error, a permutation distance over subplan cardinalities with the same join count, weighting errors by magnitude and prioritizing small multi-way joins. Used within a standard decision tree, L1-error accurately identifies sub-optimal plans across four benchmarks, with gains when combined with Q-error as a low-overhead composite feature. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Yesdaulet Izenov (University of California Merced)
- 2. Asoke Datta (University of California Merced)
- 3. Brian Tsan (University of California Merced)
- 4. Florin Rusu (University of California Merced)
BibTeX Citation
@inproceedings{izenov_sigmod24,
title = {{Sub-optimal Join Order Identification with L1-error}},
author = {Izenov, Yesdaulet and Datta, Asoke and Tsan, Brian and Rusu, Florin},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639272},
url = {https://dl.acm.org/doi/10.1145/3639272},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 26 of 26 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 | 566 | Towards a Robust Query Optimizer: A Principled and Practical Approach | 2005 | SIGMOD |
| 2 | 5,137 | Exact Cardinality Query Optimization with Bounded Execution Cost | 2019 | SIGMOD |
| 3 | 2,723 | Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation | 2022 | VLDB |
| 4 | 1,468 | Towards a Learning Optimizer for Shared Clouds | 2019 | VLDB |
| 5 | 1,876 | Flow-Loss: Learning Cardinality Estimates That Matter | 2021 | VLDB |
| 6 | 1,061 | Are We Ready For Learned Cardinality Estimation? | 2021 | VLDB |
| 7 | 2,991 | FactorJoin: A New Cardinality Estimation Framework for Join Queries | 2023 | SIGMOD |
| 8 | 1,122 | Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation | 2022 | VLDB |
| 9 | 388 | Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors | 2009 | VLDB |
| 10 | 18 | How Good Are Query Optimizers, Really? | 2016 | VLDB |