Demonstration of Accelerating Machine Learning Inference Queries with Correlative Proxy Models
Summary: Demonstrates CORE, a query optimizer building correlative proxy models online to exploit predicate correlations and speed ML inference on unstructured data. Allocates resources and reorders proxies to filter costly UDFs, beating PP. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhihui Yang
- 2. Yicong Huang
- 3. Zuozhi Wang
- 4. Feng Gao
- 5. Yao Lu
- 6. Chen Li
- 7. X. Sean Wang
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,677 | Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving | 2025 | SIGMOD | 4.3047774e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 224 | CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies | 2004 | SIGMOD | 0.00032746205 |
| 329 | Accelerating Machine Learning Inference with Probabilistic Predicates | 2018 | SIGMOD | 0.00027249545 |
| 1,043 | Adaptive Ordering of Pipelined Stream Filters | 2004 | SIGMOD | 0.00014476247 |
| 1,574 | Approximate Query Processing: No Silver Bullet | 2017 | SIGMOD | 0.00011287495 |
| 3,558 | Approximate Selection with Guarantees using Proxies | 2020 | VLDB | 6.9765724e-05 |
| 5,072 | Optimizing Machine Learning Inference Queries with Correlative Proxy Models | 2022 | VLDB | 5.7185674e-05 |
| 11,619 | Demonstration of Interactive Runtime Debugging of Distributed Dataflows in Texera | 2020 | VLDB | 4.1945683e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,407 | End-to-end Optimization of Machine Learning Prediction Queries | 2022 | SIGMOD | 7.1295646e-05 |
| 4,014 | Exploiting Correlations for Expensive Predicate Evaluation | 2015 | SIGMOD | 6.5273084e-05 |
| 11,650 | Query-Driven Learning for Next Generation Predictive Modeling & Analytics | 2019 | SIGMOD | 4.1945683e-05 |
| 4,712 | Accelerating Approximate Aggregation Queries with Expensive Predicates | 2021 | VLDB | 5.9787986e-05 |
| 3,824 | Correlation Sketches for Approximate Join-Correlation Queries | 2021 | SIGMOD | 6.7260705e-05 |
| 6,590 | Interactive Demonstration of Probabilistic Predicates | 2018 | SIGMOD | 5.0010949e-05 |
| 9,351 | On Efficient Approximate Queries over Machine Learning Models | 2023 | VLDB | 4.3524472e-05 |
| 11,426 | Accelerating Queries over Unstructured Data with ML | 2021 | CIDR | 4.1945683e-05 |
| 329 | Accelerating Machine Learning Inference with Probabilistic Predicates | 2018 | SIGMOD | 0.00027249545 |
| 5,072 | Optimizing Machine Learning Inference Queries with Correlative Proxy Models | 2022 | VLDB | 5.7185674e-05 |