Bibliographic record
Abstract
The utilizing of semi-join is often a common starting point for join algorithms in distributed databases. It helps reduce the quantity of data transferred between sites. In our thesis, we propose an algorithm, based on the semi-join operator. By utilizing the maximum reduction capability of the semi-join operation, we use our algorithm to reduce the query relations as much as possible. In order to improve the reduction ability of our algorithm, we combine composite semi-joins into our algorithm. Usually, composite semi-join may produce more reduction than separate simple semi-joins in our algorithm with more time costs. Although a composite semi-join itself may not be beneficial because of its more total time costs, it always is gainful to the execution of subsequent join operations. Our proposed algorithm is evaluated objectively against the effects of a full reducer and the total cost of initial feasible solution (IFS). It has been shown that the algorithm gives substantial reductions on relations and total costs. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .L443. Source: Masters Abstracts International, Volume: 40-03, page: 0724. Adviser: J. Morrissey. Thesis (M.Sc.)--University of Windsor (Canada), 2001.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".