OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning
Summary: OBELISK reframes offline plan management as optimizing cost-scaling knobs that steer CQO toward robust plans, instead of directly searching the plan space. Training-free closed loop: Bayesian optimization guides knob subspaces, LM reasoning proposes configs, and history-aware gating cuts redundant evaluations. (summarized by gpt-5.4-mini on May 27 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Zhicheng Pan
- 2. Wenwen Sun
- 3. Yuanjia Zhang
- 4. Terence Purcell
- 5. Yu Dong
- 6. Chengcheng Yang
- 7. Rong Zhang
- 8. Xuan Zhou
- 9. Jianliang Xu
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 1 of 51 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,957 | How to Optimize SQL Queries? A Comparison Between Split, Holistic, and Hybrid Approaches | 2025 | VLDB | 4.2373024e-05 |