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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)

Paper ID
14138
Venue
VLDB
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,801 | 24.86%
DOI
10.14778/3750601.3750655

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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
690 An Analytical Study of Large SPARQL Query Logs 2018 VLDB 0.00018099792
10,564 PlanRGCN: Predicting SPARQL Query Performance 2025 VLDB 4.1945683e-05
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