Optimization of query streams using semantic prefetching
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Bibliographic record
Abstract
Streams of relational queries submitted by client applications to database servers contain patterns that can be used to predict future requests. We present the Scalpel system, which detects these patterns and optimizes request streams using context-based predictions of future requests. Scalpel uses its predictions to provide a form of semantic prefetching, which involves combining a predicted series of requests into a single request that can be issued immediately. Scalpel's semantic prefetching reduces not only the latency experienced by the application but also the total cost of query evaluation. We describe how Scalpel learns to predict optimizable request patterns by observing the application's request stream during a training phase. We also describe the types of query pattern rewrites that Scalpels cost-based optimizer considers. Finally, we present empirical results that show the costs and benefits of Scalpel's optimizations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it