A Novel Aggregation Algorithm for Online Analytical Processing Query Evaluation
Why this work is in the frame
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Bibliographic record
Abstract
The OLAP (online analytical processing query) queries are ad-hoc, complex queries, as expressed in SQL, these queries include multi-table join and aggregate operation. In this paper, a novel sorting based aggregation algorithm, MuSA (sort-based aggregation with multi-table join), is given for OLAP query evaluation. In this algorithm, by taking the characteristics of star schema into consideration, the aggregation operation is combined with a novel multi-table join algorithm, MJoin, and the key words mapping technique is used to compress the sorting key which can obviously speed up sorting. Further by estimating the group number of query result, the proper sorting methods which can optimize the algorithm for different aggregation queries can be chosen. As being illustrated by the experimental result, compared with original methods for aggregation query evaluation, the performance of the new algorithm can be improved dramatically.
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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