UDAO: A Next-Generation Unified Data Analytics Optimizer
Summary: UDAO auto-tunes runtime configurations for general dataflow analytics to meet user objectives (speed, cost). It uses in-situ modeling and Pareto-based multi-objective optimization to expose tradeoffs and demonstrate end-to-end gains over default or hand-tuned configurations. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Khaled Zaouk
- 2. Fei Song
- 3. Chenghao Lyu
- 4. Arnab Sinha
- 5. Yanlei Diao
- 6. Prashant Shenoy
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,368 | Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing | 2022 | VLDB | 5.5457532e-05 |
| 8,617 | A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning | 2024 | VLDB | 4.4846425e-05 |
| 10,259 | Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink | 2026 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 183 | Automatic Database Management System Tuning Through Large-scale Machine Learning | 2017 | SIGMOD | 0.00036721403 |
| 868 | Profiling, What-if Analysis, and Cost-based Optimization of MapReduce Programs | 2011 | VLDB | 0.00015789681 |
| 5,075 | An Incremental Anytime Algorithm for Multi-Objective Query Optimization | 2015 | SIGMOD | 5.7172118e-05 |
| 9,504 | Supporting Scalable Analytics with Latency Constraints | 2015 | VLDB | 4.3341665e-05 |
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