Bibliographic record
Abstract
The twig join, which is used to find all occurrences of a twig pattern in an XML database, is a core operation for XML query processing. A great many strategies for handling this problem have been proposed and can be roughly classified into two groups. The first group decomposes a twig pattern (a small tree) into a set of binary relationships between pairs of nodes, such as parent-child and ancestor-descendant relations; and transforms a tree matching problem into a series of simple relation look-ups. The second group decomposes a twig pattern into a set of paths. Among all this kind of methods, the approach based on the so-called stack encoding by Bruno et. al. [2] is very interesting, which can represent in linear space a potentially exponential (in the number of query nodes) number of matching paths. However, the available processes for generating such compressed paths suffer some redundancy and can be significantly improved. In this paper, we analyze this method and show that the time complexities of path generation in its two main procedures: PathStack and TwigStack can be reduced from O(m2 ·n) to O(m ·n), where m and n are the sizes of the query tree and document tree, respectively. Experiments have been done to compare ours and some existing startegies, which shows that using our method much less time is needed to generate matching paths.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".