Optimization of Distributed SPARQL Queries Using Edmonds' Algorithm and Prim's Algorithm
Bibliographic record
Abstract
Here we describe the application of two well known graph algorithms, Edmonds' algorithm and Prim's algorithm, to the problem of optimizing distributed SPARQL queries. In the context of this paper, a ldquodistributed SPARQL queryrdquo is a SPARQL query which is resolved by contacting any number of remote SPARQL endpoints. Two optimization approaches are described. In the first approach, a static query plan is computed in advance of query execution, using one of two standard graph algorithms for finding minimum spanning trees (Edmonds' algorithm and Prim's algorithm). In the second approach, the planning and execution of the query are interleaved, so that as each potential solution is expanded it is permitted to follow an independent query plan. Our optimization approach requires basic statistics regarding RDF predicates which must be obtained prior to the user's query, through automated querying of the remote SPARQL endpoints.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".