Unordered tree matching and ordered tree matching: the evaluation of tree pattern queries
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
In this paper, we study the twig pattern matching in XML document databases. Two algorithms A1 and A2 are discussed according to two different definitions of tree embedding. By the first definition, only the ancestor-descendant relationship is considered. By the second one, we take not only the ancestor-descendant relationship, but also the order of siblings into account. Both A1 and A2 are based on a subtree reconstruction technique, by which a tree structure is reconstructed according to a given set of data streams. More importantly, by revealing an interesting property of tree encoding, we show that the subtree reconstruction can be easily extended to a strategy (i.e., A1) for checking subtree matching according to the first definition with any kind of path join or join-like operations being completely avoided. A2 needs more time and space since it deals with a more difficult problem, but without join operations involved, either. The computational complexities of both algorithms are analysed, showing that they have a better performance than any existing strategy for this problem.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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