Using partial evaluation in distributed query evaluation
Bibliographic record
Abstract
A basic idea in parallel query processing is that one is prepared to do more computation than strictly necessary at individual sites in order to reduce the elapsed time, the network traffic, or both in the evaluation of the query. We develop this idea for the evaluation of boolean XPath queries over a tree that is fragmented, both horizontally and vertically over a number of sites. The key idea is to send the whole query to each site which partially evaluates, in parallel, the query and sends the results as compact boolean functions to a coordinator which combines these to obtain the result. This approach has several advantages. First, each site is visited only once, even if several fragments of the tree are stored at that site. Second, no prior constraints on how the tree is decomposed are needed, nor is any structural information about the tree required, such as a DTD. Third, there is a satisfactory bound on the total computation performed on all sites and on the total network traffic. We also develop a simple incremental maintenance algorithm that requires communication only with the sites at which changes have taken place; moreover the network traffic depends neither on the data nor on the update. These results, we believe, illustrate the usefulness and potential of partial evaluation in distributed systems as well as centralized xml stores for evaluating XPath queries and beyond.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".