DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search
Summary: DARTH enables declarative recall targets for approximate nearest neighbor search by embedding adaptive early termination into the search process. It reduces tuning effort and achieves large speedups (HNSW up to 14.6x; IVF up to 41.8x) while meeting user recall targets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Manos Chatzakis (Université Paris Cité)
- 2. Yannis Papakonstantinou (Google)
- 3. Themis Palpanas (Université Paris Cité)
BibTeX Citation
@inproceedings{chatzakis_sigmod26,
title = {{DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search}},
author = {Chatzakis, Manos and Papakonstantinou, Yannis and Palpanas, Themis},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3749160},
url = {https://dl.acm.org/doi/10.1145/3749160},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,193 | An In-Depth Study of Filter-Agnostic Vector Search on a PostgreSQL Database System: [Experiments & Analysis] | 2026 | SIGMOD | 5.093636e-05 |
| 10,297 | TaCo: Data-adaptive and Query-aware Subspace Collision for High-dimensional Approximate Nearest Neighbor Search | 2026 | SIGMOD | 5.093636e-05 |
| 10,336 | Accelerating High-Dimensional ANN Search via Skipping Redundant Distance Computations | 2026 | SIGMOD | 5.093636e-05 |
| 10,443 | Distribution-Aware Exploration for Adaptive HNSW Search | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 20 of 20 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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