Reduction filters for minimizing data transfers in distributed query optimization
Why this work is in the frame
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Bibliographic record
Abstract
It has long been recognized that query optimization in distributed database systems is an important research issue. The challenge is to determine a sequence of operations which will process the query while minimizing the chosen cost function. Finding the optimal optimization for a general query is an NP-hard problem so, in general, heuristics are employed to find a cost-effective and efficient processing method. We present a novel approach to the problem, which uses reduction filters, with the objective of minimizing the total volume of data transferred in the network. We assume a distributed relational database management system and select-project-join queries. This means that we have a number of relations, each located at a different site in the network, which must be joined and the result made available at some distinct query site. Our technique is to reduce the relations, before shipment to the query site, using reduction filters and thereby significantly reduce the total communication cost.
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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 | 0.002 |
| 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