Advanced Join Strategies for Large-Scale Distributed Computation
Summary: Proposes novel distributed join strategies for cloud-scale clusters, tackling data movement and parallelism in massive clusters. Introduces robust data-skew handling and exploits opportunities unavailable to centralized DBMSs, validated on Scope clusters. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Nicolas Bruno
- 2. YongChul Kwon
- 3. Ming-Chuan Wu
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|---|---|---|---|---|
| 11,358 | Scaling Equi-Joins | 2022 | SIGMOD | 4.1945683e-05 |
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| 8,781 | Accelerate Distributed Joins with Predicate Transfer | 2025 | SIGMOD | 4.4534753e-05 |
| 4,061 | Advanced Partitioning Techniques for Massively Distributed Computation | 2012 | SIGMOD | 6.483587e-05 |
| 3,443 | Distributed Join Algorithms on Thousands of Cores | 2017 | VLDB | 7.0887214e-05 |
| 1,939 | From Theory to Practice: Efficient Join Query Evaluation in a Parallel Database System | 2015 | SIGMOD | 0.00010025655 |
| 11,890 | Let's Rethink Join Optimization in Distributed Systems | 2015 | CIDR | 4.1945683e-05 |
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| 5,297 | Continuous Cloud-Scale Query Optimization and Processing | 2013 | VLDB | 5.5801669e-05 |