Facilitating SQL Query Composition and Analysis
Summary: Predicts pre-execution query properties to accelerate SQL tuning without DB statistics or execution plans. Data-driven neural models trained on large query workloads estimate answer size, runtime, and error class, empirically outperforming statistics- and plan-based baselines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,429 | Real-time Workload Pattern Analysis for Large-scale Cloud Databases | 2023 | VLDB | 7.1010535e-05 |
| 3,522 | ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases | 2021 | SIGMOD | 7.0096727e-05 |
| 4,804 | Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload | 2021 | SIGMOD | 5.910467e-05 |
| 4,842 | Towards Dynamic and Safe Configuration Tuning for Cloud Databases | 2022 | SIGMOD | 5.8826802e-05 |
| 8,884 | Workload Insights From The Snowflake Data Cloud: What Do Production Analytic Queries Really Look Like? | 2025 | VLDB | 4.4283999e-05 |
| 9,203 | Intelligent Automated Workload Analysis for Database Replatforming | 2022 | SIGMOD | 4.3740313e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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