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)
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Authors
- 1. Udesh Kumarasinghe (Purdue University)
- 2. Tyler Liu (Purdue University)
- 3. Ahmed Mahmood (Google)
- 4. Chunwei Liu (Purdue University)
- 5. Walid G. Aref (Purdue University)
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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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 71 | DuckDB: an Embeddable Analytical Database | 2019 | SIGMOD | 0.00037720227 |
| 125 | Predicate Migration: Optimizing Queries with Expensive Predicates | 1993 | SIGMOD | 0.00030470848 |
| 683 | DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing | 2025 | VLDB | 0.00014817539 |
| 748 | Palimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing | 2025 | CIDR | 0.00014281926 |
| 5,402 | Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB | 2025 | VLDB | 6.1461781e-05 |
| 5,551 | Aero: Adaptive Query Processing of ML Queries | 2025 | SIGMOD | 6.0872198e-05 |
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