An algorithm for tree-query membership of a distributed query
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim is to process distributed queries ef ficiently. The cost of communications between sites is dominant in processing such queries. It is assumed that the amount of data transferred determines the transmission cost to a large extent. Thus, it is desirable to minimize the amount of transmitted data. Bernstein-and Chiu [2] classified queries into two types: tree and cyclic queries. They defined an operation called semi-join which requires minimal transfer of data between sites. Then they showed that tree queries can always be answered by semi-joins but cyclic queries may not. An algorithm to decide whether a query is cyclic or not was presented in their paper. Their algorithm works when the number of domains in common between any two relations is no more than one. The aim of this paper is to generalize their algorithm. Specifically, we present a conceptionally simple algorithm which decides the type of a query when the number of domains in common between two relations may exceed one. An implementation of the algorithm is outlined. The algorithm runs in 0(max(e,e')) time and O(e) space complexity where e and e' are the number of edges in the transitive closure of the join graph and the query graph respectively.
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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