Bibliographic record
Abstract
With the growing importance of XML in data exchange, much research has been done in providing flexible query mechanisms to extract data from XML documents. A core operation for XML query processing is to find all occurrences of a twig pattern Q (or small tree) in a document T. Prior work has typically decomposed Q into binary structural relationships, such as parent-child and ancestor-descendant relations, or root-to-leaf paths. The twig matching is achieved by: (a) matching the binary relationships or paths against XML databases, and (b) using the join algorithms to stitch together all the matching binary relationships or paths. In the worst case, the time for doing joins can be exponential (in the number of query nodes or decomposed paths). In this chapter, we discuss a new algorithm for this task with no path joins involved. The time and space complexities of the algorithm are bounded by O (|T|·Qleaf) and O (Tleaf·Qleaf), respectively, where Tleaf stands for the number of the leaf nodes in T and Qleaf for the number of the leaf nodes in Q. Our experiments show that our method is efficient in supporting twig pattern queries.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".