Optimization of Large Join Queries: Combining Heuristics and Combinatorial Techniques
Summary: Large-join optimization via hybrids of augmentation and local-improvement heuristics, paired with iterative improvement and simulated annealing. Systematic experiments under two cost models and multiple synthetic benchmarks identify augmentation+iterative improvement as the strongest combination in most scenarios. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Arun Swami
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Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1 | Access Path Selection in a Relational Database Management System | 1979 | SIGMOD | 0.0040465394 |
| 9 | Implementation Techniques For Main Memory Database Systems | 1984 | SIGMOD | 0.001426258 |
| 30 | Hashing Methods and Relational Algebra Operations | 1984 | VLDB | 0.00078665259 |
| 144 | Optimization of Nonrecursive Queries | 1986 | VLDB | 0.00041430126 |
| 388 | Optimization of Large Join Queries | 1988 | SIGMOD | 0.00024654816 |
| 569 | Query Optimization by Simulated Annealing | 1987 | SIGMOD | 0.00019912758 |
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| 71 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00059446482 |
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| 400 | Randomized Algorithms For Optimizing Large Join Queries | 1990 | SIGMOD | 0.00024308369 |
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