Aker: Density-Aware Approximate Caching for Vector Search
Summary: Aker is a density-aware approximate cache for disk-based ANNS, using per-query thresholds that adapt to local neighbor density for higher recall and throughput. Its del-consistency model eagerly applies deletions and lazily refreshes insertions, bounding staleness at low cost. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Sukjoon Oh (Korea Advanced Institute of Science and Technology)
- 2. Minki Kang (Korea Advanced Institute of Science and Technology)
- 3. Dohyun Kim (Korea Advanced Institute of Science and Technology)
- 4. Baotong Lu (Microsoft)
- 5. Jing Liu (Microsoft)
- 6. Qianxi Zhang (Microsoft)
- 7. Qi Chen (Microsoft)
- 8. Youjip Won (Korea Advanced Institute of Science and Technology)
BibTeX Citation
@article{oh_vldb26,
title = {{Aker: Density-Aware Approximate Caching for Vector Search}},
author = {Oh, Sukjoon and Kang, Minki and Kim, Dohyun and Lu, Baotong and Liu, Jing and Zhang, Qianxi and Chen, Qi and Won, Youjip},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {10},
pages = {2727--2740},
doi = {10.14778/3828612.3828627},
url = {https://doi.org/10.14778/3828612.3828627},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 194 | Milvus: A Purpose-Built Vector Data Management System | 2021 | SIGMOD | 0.00025636725 |
| 341 | AnalyticDB-V: A Hybrid Analytical Engine Towards Query Fusion for Structured and Unstructured Data | 2020 | VLDB | 0.00020539791 |
| 916 | PASE: PostgreSQL Ultra-High-Dimensional Approximate Nearest Neighbor Search Extension | 2020 | SIGMOD | 0.00013094482 |
| 1,613 | Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment | 2024 | SIGMOD | 0.00010072237 |
| 2,085 | SingleStore-V: An Integrated Vector Database System in SingleStore | 2024 | VLDB | 9.0709364e-05 |
| 2,503 | FEXIPRO: Fast and Exact Inner Product Retrieval in Recommender Systems | 2017 | SIGMOD | 8.3786151e-05 |
| 2,770 | Constructing and Analyzing the LSM Compaction Design Space | 2021 | VLDB | 8.037607e-05 |
| 9,099 | Similarity Caching | 2009 | PODS | 5.2283159e-05 |
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