PlanRGCN: Predicting SPARQL Query Performance
Summary: PlanRGCN: a graph-convolution model that predicts SPARQL runtimes from query plans and KG statistics, generalizing to unseen structures and supporting non-trivial SPARQL operators. Scalable pretraining; improves load‑balancing throughput up to 207% and execution by up to 70%. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Abiram Mohanaraj (Aalborg University)
- 2. Matteo Lissandrini (University of Verona)
- 3. Katja Hose (Vienna University of Technology)
BibTeX Citation
@article{mohanaraj_vldb25,
title = {{PlanRGCN: Predicting SPARQL Query Performance}},
author = {Mohanaraj, Abiram and Lissandrini, Matteo and Hose, Katja},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {6},
pages = {1621--1634},
doi = {10.14778/3725688.3725694},
url = {https://doi.org/10.14778/3725688.3725694},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,028 | Smart SPARQL Advisor: Guiding Users in Query Formulation with Performance Prediction | 2025 | VLDB | 5.093636e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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