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 (University of Michigan)
- 2. Michael R. Anderson (University of Michigan)
- 3. Maxwell Strome (University of Michigan)
- 4. Michael Cafarella (University of Michigan)
BibTeX Citation
@inproceedings{he_sigmod20,
title = {{A Method for Optimizing Opaque Filter Queries}},
author = {He, Wenjia and Anderson, Michael R. and Strome, Maxwell and Cafarella, Michael},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389766},
url = {https://dl.acm.org/doi/10.1145/3318464.3389766},
year = {2020}
}
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