Generating efficient execution plans for vertically partitioned XML databases
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Bibliographic record
Abstract
Experience with relational systems has shown that distribution is an effective way of improving the scalability of query evaluation. In this paper, we show how distributed query evaluation can be performed in a vertically partitioned XML database system. We propose a novel technique for constructing distributed execution plans that is independent of local query evaluation strategies. We then present a number of optimizations that allow us to further improve the performance of distributed query execution. Finally, we present a response time-based cost model that allows us to pick the best execution plan for a given query and database instance. Based on an implementation of our techniques within a native XML database system, we verify that our execution plans take advantage of the parallelism in a distributed system and that our cost model is effective at identifying the most advantageous plans.
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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 | 0.000 |
| Open science | 0.000 | 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