A Method for Optimizing Opaque Filter Queries
Summary: Proposes voodoo indexing for opaque filter queries with unknown UDF semantics. Offline, builds a query-independent hierarchical index; online, maps group satisfaction to accelerate execution without in-query training, delivering up to 88% speedup over scans and 79% over prior best. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Wenjia He
- 2. Michael R. Anderson
- 3. Maxwell Strome
- 4. Michael Cafarella
Incoming Citations (Sorted by Pagerank)
Showing 12 of 12 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,041 | DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning | 2022 | VLDB | 4.5998045e-05 |
| 6,590 | Interactive Demonstration of Probabilistic Predicates | 2018 | SIGMOD | 5.0010949e-05 |
| 3,407 | End-to-end Optimization of Machine Learning Prediction Queries | 2022 | SIGMOD | 7.1295646e-05 |
| 11,426 | Accelerating Queries over Unstructured Data with ML | 2021 | CIDR | 4.1945683e-05 |
| 10,950 | PLAQUE: Automated Predicate Learning at Query Time | 2024 | SIGMOD | 4.1945683e-05 |
| 4,994 | Stacked Filters: Learning to Filter by Structure | 2021 | VLDB | 5.78027e-05 |
| 10,471 | Approximating Opaque Top-k Queries | 2025 | SIGMOD | 4.1945683e-05 |
| 9,807 | Demonstration of Accelerating Machine Learning Inference Queries with Correlative Proxy Models | 2022 | VLDB | 4.2805224e-05 |
| 5,072 | Optimizing Machine Learning Inference Queries with Correlative Proxy Models | 2022 | VLDB | 5.7185674e-05 |
| 329 | Accelerating Machine Learning Inference with Probabilistic Predicates | 2018 | SIGMOD | 0.00027249545 |