Bibliographic record
Abstract
With the growing importance of XML in data exchange, much research has been done in providing flexible query mechanisms to extract data from XML documents. A core operation for XML query processing is to find all occurrences of a twig pattern Q (or small tree) in a document T. Prior work has typically decomposed Q into binary structural relationships, such as parent-child and ancestor-descendant relations, or root-to-leaf paths. The twig matching is achieved by: (a) matching the binary relationships or paths against XML databases, and (b) using the join algorithms to stitch together all the matching binary relationships or paths. In the worst case, the time for doing joins can be exponential (in the number of query nodes or decomposed paths). In this chapter, we discuss a new algorithm for this task with no path joins involved. The time and space complexities of the algorithm are bounded by O (|T|·Qleaf) and O (Tleaf·Qleaf), respectively, where Tleaf stands for the number of the leaf nodes in T and Qleaf for the number of the leaf nodes in Q. Our experiments show that our method is efficient in supporting twig pattern queries.
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 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 | 0.001 | 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".