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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".