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MicroNN: An On-device Disk-resident Updatable Vector Database

Summary: On-device, disk-resident vector search for updatable workloads with hybrid queries (NN + attribute filters) under tight memory. Embeddable MicroNN supports continuous inserts/deletes and delivers ~7 ms top-100 with 90% recall on a million-scale benchmark using ~10 MB RAM. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7169
Venue
SIGMOD
Year
2025
Pagerank
5.1955087e-05
Overall Rank
9,915 | 31.98%
DOI
10.1145/3722212.3724444

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{pound_sigmod25,
        title = {{MicroNN: An On-device Disk-resident Updatable Vector Database}},
        author = {Pound, Jeffrey and Chabert, Floris and Bhushan, Arjun and Goswami, Ankur and Pacaci, Anil and Chowdhury, Shihabur Rahman},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3722212.3724444},
        url = {https://dl.acm.org/doi/10.1145/3722212.3724444},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,251 GPS: Revisiting the Data Layout for Disk-based High-Dimensional Vector Search 2026 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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