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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".