Query-Driven Learning for Next Generation Predictive Modeling & Analytics
Summary: Query-driven learning to democratize analytics: learn lightweight ML models from query workloads that run off-cloud. Unique focus on local, resource-aware AQP for analytic aggregates (COUNT/MIN/MAX), enabling accurate approximations with low cost by adapting models on-the-fly. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Fotis Savva
Incoming Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 14 | Online Aggregation | 1997 | SIGMOD | 0.0010801504 |
| 716 | Query-based Workload Forecasting for Self-Driving Database Management Systems | 2018 | SIGMOD | 0.00017723171 |
| 1,323 | Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters | 2016 | SIGMOD | 0.00012601997 |
| 1,552 | Overview of Data Exploration Techniques | 2015 | SIGMOD | 0.00011408814 |
| 2,355 | G-OLA: Generalized On-Line Aggregation for Interactive Analysis on Big Data | 2015 | SIGMOD | 8.9677847e-05 |
| 2,588 | Database Learning: Toward a Database that Becomes Smarter Every Time | 2017 | SIGMOD | 8.4909562e-05 |
| 5,413 | QUIET: Continuous Query-driven Index Tuning | 2003 | VLDB | 5.5203159e-05 |
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| 9,351 | On Efficient Approximate Queries over Machine Learning Models | 2023 | VLDB | 4.3524472e-05 |