A Fast Randomized Algorithm for Multi-Objective Query Optimization
Summary: First polynomial-time, randomized algorithm for multi-objective query optimization. Iterative, multi-objective hill climbing with multiple transformations and a Pareto-cache of intermediate results enables scalable search over join orders and outperforms NSGA-II on large queries. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Immanuel Trummer (EPFL)
- 2. Christoph Koch (EPFL)
BibTeX Citation
@inproceedings{trummer_sigmod16,
title = {{A Fast Randomized Algorithm for Multi-Objective Query Optimization}},
author = {Trummer, Immanuel and Koch, Christoph},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2882927},
url = {https://dl.acm.org/doi/10.1145/2882903.2882927},
year = {2016}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,468 | Towards a Learning Optimizer for Shared Clouds | 2019 | VLDB | 0.00010686496 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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