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Record W4214690579 · doi:10.1109/wi.2004.10048

A Fast Tree Pattern Matching Algorithm for XML Query

2005· article· en· W4214690579 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE/WIC/ACM International Conference on Web Intelligence (WI'04) · 2005
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Database Systems and Queries
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPattern matchingComputer scienceXMLMatching (statistics)Tree (set theory)Tree decompositionAlgorithmTheoretical computer scienceMathematicsArtificial intelligenceCombinatorics

Abstract

fetched live from OpenAlex

Finding all distinct matchings of the query tree pattern is the core operation of XML query evaluation. The existing methods for tree pattern matching are decomposition-matching-merging processes, which may produce large useless intermediate result or require repeated matching of some sub-patterns. We propose a fast tree pattern matching algorithm called TreeMatch to directly £nd all distinct matchings of a query tree pattern. The only requirement for the data source is that the matching elements of the non-leaf pattern nodes do not contain sub-elements with the same tag. The TreeMatch does not produce any intermediate results and the £nal results are compactly encoded in stacks, from which the explicit representation can be produced ef£ciently.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.793
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.057
GPT teacher head0.325
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it