Unordered tree matching and ordered tree matching: the evaluation of tree pattern queries
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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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".