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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)

Paper ID
5118
Venue
SIGMOD
Year
2016
Pagerank
4.4600243e-05
Overall Rank
8,725 | 39.31%
DOI
10.1145/2882903.2882927

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Rank Citing Paper Year Venue Pagerank
2,083 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 9.5834572e-05
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