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Metric Algebra: Embedding-Independence in Vector Databases

Summary: Metric algebra makes cross-embedding search well-defined by modeling datasets as metric relations and defining semantics-preserving Union across vector spaces. MetricDB realizes Union through learned witness maps, planning for imperfect mappings and reusing indexes without re-embedding or rebuilding. (summarized by gpt-6-luna on Oct 22 2026)

Paper ID
h70a3b4430c3e4913
Venue
SIGMOD
Year
2027
Pagerank
4.9605343e-05
Overall Rank
10,402 | 30.28%
DOI
10.1145/3837117
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BibTeX Citation

@inproceedings{yang_sigmod27,
        title = {{Metric Algebra: Embedding-Independence in Vector Databases}},
        author = {Yang, Tianjian and Cao, Yang and Yang, Beining and Chen, Ziying and Ma, Tiejun},
        series = {{SIGMOD} '27},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3837117},
        url = {https://dl.acm.org/doi/10.1145/3837117},
        year = {2027}
}

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