Optimizing Correlated Path Queries in XML Languages
Why this work is in the frame
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Bibliographic record
Abstract
Path expressions are ubiquitous in XML processing languages such as XPath, XQuery, and XSLT. Expressions in these languages typically include multiple path expressions, some of them correlated. Existing approaches evaluate these path expressions one-at-a-time and miss the optimization opportunities that may be gained by exploiting the correlations among them. In this paper, we address the evaluation and optimization of correlated path expressions. In particular, we propose two types of optimization techniques: integrating correlated path expressions into a single pattern graph, and rewriting the pattern graph according to a set of rewriting rules. The first optimization technique allows the query optimizer to choose an execution plan that is impossible by using the existing approaches. The second optimization technique rewrites pattern graphs at a logical level and produce a set of equivalent pattern graphs from which a physical optimizer can choose given an appropriate cost function. Under certain conditions that we identify, the graph pattern matching-based execution approach that we propose may be more efficient than the join-based approaches.
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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 | 0.001 |
| 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