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Adaptive Optimization of Very Large Join Queries

Summary: Adaptive optimization for large join queries: exact solutions for typical sizes, scalable to thousands of joins. Novel search-space linearization yields near-optimal plans for large joins; implementation tricks and experiments across diverse sizes. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5515
Venue
SIGMOD
Year
2018
Pagerank
0.00011320736
Overall Rank
1,286 | 91.18%
DOI
10.1145/3183713.3183733

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{neumann_sigmod18,
        title = {{Adaptive Optimization of Very Large Join Queries}},
        author = {Neumann, Thomas and Radke, Bernhard},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3183733},
        url = {https://dl.acm.org/doi/10.1145/3183713.3183733},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 26 of 26 citing papers.

Rank Citing Paper Year Venue Pagerank
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
103 DuckDB: an Embeddable Analytical Database 2019 SIGMOD 0.00034161428
809 Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal Joins 2019 VLDB 0.00013874588
1,499 Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities 2019 SIGMOD 0.00010564536
2,161 DIFF: A Relational Interface for Large-Scale Data Explanation 2019 VLDB 9.0606664e-05
2,202 Quantifying TPC-H Choke Points and Their Optimizations 2020 VLDB 8.9639459e-05
3,018 The LDBC Social Network Benchmark: Business Intelligence Workload 2023 VLDB 7.8473755e-05
3,205 On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML 2018 VLDB 7.6386536e-05
4,081 Abacus: A Cost-Based Optimizer for Semantic Operator Systems 2026 VLDB 6.9165634e-05
4,409 MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions 2019 SIGMOD 6.7178579e-05
5,399 Efficient Massively Parallel Join Optimization for Large Queries* 2022 SIGMOD 6.2319315e-05
5,626 Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware 2023 SIGMOD 6.1440728e-05
6,439 DuckPGQ: Bringing SQL/PGQ to DuckDB 2023 VLDB 5.8787285e-05
6,735 Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges 2023 VLDB 5.7878855e-05
7,156 The Case for Deep Query Optimisation 2020 CIDR 5.686096e-05
7,386 Instance-Optimal Acyclic Join Processing Without Regret: Engineering the Yannakakis Algorithm in Column Stores 2025 VLDB 5.6273882e-05
7,882 Efficiently Computing Join Orders with Heuristic Search 2023 SIGMOD 5.5237338e-05
7,910 Quantum-Inspired Digital Annealing for Join Ordering 2024 VLDB 5.5181056e-05
8,572 T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees 2025 SIGMOD 5.4102362e-05
9,042 DPconv: Super-Polynomially Faster Join Ordering 2024 SIGMOD 5.3256042e-05
9,969 Hybrid Mixed Integer Linear Programming for Large-Scale Join Order Optimisation 2026 VLDB 5.1845938e-05
10,232 EncoderForge: Generating Efficient SQL for Encoders in Machine Learning Inference Pipelines 2026 SIGMOD 5.093636e-05
10,296 Succinct Structure Representations for Efficient Query Optimization 2026 SIGMOD 5.093636e-05
11,206 Understanding and Reusing Test Suites Across Database Systems 2024 SIGMOD 5.093636e-05
11,421 Lightweight Materialization for Fast Dashboards Over Joins 2023 SIGMOD 5.093636e-05
11,453 Asymptotically Better Query Optimization Using Indexed Algebra 2023 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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