MétaCan
Menu
Back to cohort
Record W2015071642 · doi:10.1145/1030397.1030401

Querying XML documents by dynamic shredding

2004· article· en· W2015071642 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Database Systems and Queries
Canadian institutionsUniversity of WaterlooUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceXML Schema EditorStreaming XMLEfficient XML InterchangeXML validationXML databaseXQueryDocument Structure DescriptionInformation retrievalXML Schema (W3C)XML EncryptionXMLDatabaseWorld Wide Web

Abstract

fetched live from OpenAlex

With the wide adoption of XML as a standard data representation and exchange format querying XML documents becomes increasingly important. However relational database systems constitute a much more mature technology than what is available for native storage of XML. To bridge the gap one way to manage XML data is to use a commercial relational database system. In this approach users typically first ``shred'' their documents by isolating what they predict to be meaningful fragments then store the individual fragments according to some relational schema and later translate each XML query (e.g. expressed in W3C's XQuery) to SQL queries expressed against the shredded documents.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.248
Teacher spread0.243 · 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

Quick stats

Citations12
Published2004
Admission routes2
Has abstractyes

Explore more

Same topicAdvanced Database Systems and QueriesFrench-language works237,207