An XPath Query Aggregation Algorithm Using a Region Encoding
Bibliographic record
Abstract
XML pub/sub systems have recently emerged as application-layer XML routers. XML-based network traffic is expected to have significant growth. The main problem of the state-of-the-art XML routing schemes is that XML filtering speed often cannot match the XML document arrival speed. The XML filtering speed is propotional to the number of quries which are typically represented as XPath quries. Aggregation is a useful technique for reducing the number of XPath queries. This paper will present a new XPath query aggregation algorithm based on a node region encoding scheme which provides positional information. Compared with the existing aggregation algorithms in the literature, our proposed algorithm can efficiently evaluate the ancestor-descendant and parent-child relationships between any pair of nodes in XPath queries and process a tree-structured query as a unit. Experimental results demonstrate the effectiveness of the proposed algorithm. The performance improvement for the proposed algorithm could be up to 61% compared to the existing XSearch algorithm which is the most efficient algorithm for XPath query aggregation so far.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".