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iPDB: SQL with ML and LLM Predicates (Towards a Database Engine for AI)

Summary: iPDB embeds LLM inference directly in declarative SQL, adding semantic selection, projection, and joins over structured/unstructured data. Its query-planning optimizations exploit semantic-query patterns to reduce the substantial runtime and cost of large-scale inference. (summarized by gpt-5.6-luna on Aug 28 2026)

Paper ID
hdab67f48cebea19a
Venue
VLDB
Year
2026
Pagerank
4.9793485e-05
Overall Rank
11,008 | 25.99%
DOI
10.14778/3827998.3828121

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BibTeX Citation

@article{kumarasinghe_vldb26,
        title = {{iPDB: SQL with ML and LLM Predicates (Towards a Database Engine for AI)}},
        author = {Kumarasinghe, Udesh and Liu, Tyler and Mahmood, Ahmed and Liu, Chunwei and Aref, Walid G.},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {12},
        pages = {4782--4785},
        doi = {10.14778/3827998.3828121},
        url = {https://doi.org/10.14778/3827998.3828121},
        year = {2026}
}

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