Tree Reconstruction and Bottom-Up Evaluation of Tree Pattern Queries
Bibliographic record
Abstract
An XML tree pattern query, represented as a labeled tree, is essentially a complex selection predicate on both structure and content of an XML. Tree pattern matching has been identified as a core operation in querying XML data. However, almost all the proposed algorithms only deal with unordered trees, by which the order of siblings is not considered. In this paper, we discuss a new algorithm for processing ordered tree pattern queries, for which not only the ancestor-descendant and parent-child relationships, but also the order of siblings are significant. The time complexity of the algorithm is bounded by O(|D|·|Q| + |T|·leafQ) and its space overhead is by O(leafT·leafQ), where T stands for a document tree, Q for a tree pattern query and D is a largest data stream associated with a query node q of Q, which contains the database nodes that match the node predicate at q. leafT(leafQ) represents the number of the leaf nodes of T (resp. Q). In addition, the algorithm can be adapted to an indexing environment with XB-trees being used. Experiments have been conducted, which shows that the new algorithm is promising.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".