The UDFBench Benchmark for General-purpose UDF Queries
Summary: Introduces UDFBench, the first benchmark centered on real-world UDF queries and their execution bottlenecks. Its composable workloads isolate or combine overheads, enabling cross-system evaluation of optimization and compilation techniques across five databases. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yannis Foufoulas (Athena Research Center)
- 2. Theoni Palaiologou (Athena Research Center; University of Athens)
- 3. Alkis Simitsis (Athena Research Center)
BibTeX Citation
@article{foufoulas_vldb25,
title = {{The UDFBench Benchmark for General-purpose UDF Queries}},
author = {Foufoulas, Yannis and Palaiologou, Theoni and Simitsis, Alkis},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {9},
pages = {2804--2817},
doi = {10.14778/3746405.3746409},
url = {https://doi.org/10.14778/3746405.3746409},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,723 | Scalable GPU Acceleration of Scalar Functions in Analytical Databases: Compilation, Benchmarking, and Optimization | 2026 | VLDB | 4.9793485e-05 |
| 10,932 | IMLane: Composable Framework for Efficient AI Function Execution in Database Engine | 2026 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 34 of 34 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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