Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal\n Joins
Bibliographic record
Abstract
We study the problem of optimizing subgraph queries using the new worst-case\noptimal join plans. Worst-case optimal plans evaluate queries by matching one\nquery vertex at a time using multiway intersections. The core problem in\noptimizing worst-case optimal plans is to pick an ordering of the query\nvertices to match. We design a cost-based optimizer that (i) picks efficient\nquery vertex orderings for worst-case optimal plans; and (ii) generates hybrid\nplans that mix traditional binary joins with worst-case optimal style multiway\nintersections. Our cost metric combines the cost of binary joins with a new\ncost metric called intersection-cost. The plan space of our optimizer contains\nplans that are not in the plan spaces based on tree decompositions from prior\nwork. In addition to our optimizer, we describe an adaptive technique that\nchanges the orderings of the worst-case optimal sub-plans during query\nexecution. We demonstrate the effectiveness of the plans our optimizer picks\nand adaptive technique through extensive experiments. Our optimizer is\nintegrated into the Graphflow DBMS.\n
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.000 | 0.001 |
| 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".