Smart SPARQL Advisor: Guiding Users in Query Formulation with Performance Prediction
Summary: Integrates query performance prediction into SPARQL authoring, flagging queries likely to be slow or time out before execution. When necessary, an LLM guided by QPP latent representations suggests performant reformulations and pinpoints bottlenecks to reduce idle time and unproductive executions. (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 = {{Smart SPARQL Advisor: Guiding Users in Query Formulation with Performance Prediction}},
author = {Mohanaraj, Abiram and Lissandrini, Matteo and Hose, Katja},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5295--5298},
doi = {10.14778/3750601.3750655},
url = {https://doi.org/10.14778/3750601.3750655},
year = {2025}
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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 |
|---|---|---|---|---|
| 530 | An Analytical Study of Large SPARQL Query Logs | 2018 | VLDB | 0.0001709169 |
| 10,830 | PlanRGCN: Predicting SPARQL Query Performance | 2025 | VLDB | 5.093636e-05 |
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