SIEVE: Effective Filtered Vector Search with Collection of Indexes
Summary: SIEVE replaces predicate-constrained graph traversal with a workload-aware collection of specialized indexes for filtered vector search. A three-dimensional size–latency–recall model guides index selection, yielding up to 8.06× speedups with modest memory overhead. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhaoheng Li (University of Illinois Urbana-Champaign)
- 2. Silu Huang (ByteDance)
- 3. Wei Ding (ByteDance)
- 4. Yongjoo Park (University of Illinois Urbana-Champaign)
- 5. Jianjun Chen (ByteDance)
BibTeX Citation
@article{li_vldb25,
title = {{SIEVE: Effective Filtered Vector Search with Collection of Indexes}},
author = {Li, Zhaoheng and Huang, Silu and Ding, Wei and Park, Yongjoo and Chen, Jianjun},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {4723--4736},
doi = {10.14778/3749646.3749725},
url = {https://doi.org/10.14778/3749646.3749725},
year = {2025}
}
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 |
| 10,242 | FAVOR: Efficient Filter-Agnostic Vector ANNS Based on Selectivity-Aware Exclusion Distances | 2026 | SIGMOD | 5.093636e-05 |
| 10,584 | QStore: Quantization-Aware Compressed Model Storage | 2026 | VLDB | 5.093636e-05 |
| 10,600 | Chipmink: Efficient Delta Identification for Massive Object Graphs | 2026 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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