Multiple-Query Optimization of Regular Path Queries
Why this work is in the frame
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Bibliographic record
Abstract
Graph databases have become increasingly important with the rise of social networks, and with the growth of the Semantic Web and characterization of biological networks. Regular path queries (RPQs) are a way to explore path patterns in graphs which have become a standard method to explore graph databases. SPARQL 1.1 includes property paths, and so now encompasses RPQs as a fragment. In many environments, such as visual query systems (VQSs), the RPQs are generated visually which may contain many commonalities that can be optimized globally. We introduce SWARMGUIDE, a framework for optimizing multiple regular path queries. The framework detects commonalities among the RPQs, in order to find an execution plan that is globally optimized over the plan spaces of the constituent RPQs.
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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 | 0.001 | 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