ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data
Summary: ACORN: hybrid ANN + structured search over embeddings/attributes with predicate-agnostic support. Key idea: predicate subgraph traversal on HNSW, implemented as a drop-in extension, yielding state-of-the-art throughput/recall across simple and complex mixed-modality queries. (summarized by gpt-5.4-mini on May 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Liana Patel (Stanford University)
- 2. Peter Kraft (DBOS, Inc.)
- 3. Carlos Guestrin (Stanford University)
- 4. Matei Zaharia (University of California Berkeley)
BibTeX Citation
@inproceedings{patel_sigmod24,
title = {{ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data}},
author = {Patel, Liana and Kraft, Peter and Guestrin, Carlos and Zaharia, Matei},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654923},
url = {https://dl.acm.org/doi/10.1145/3654923},
year = {2024}
}
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