ChuQL: processing XML with XQuery using Hadoop
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.
Bibliographic record
Abstract
Hadoop provides an economical tool for processing large amounts of data; its success has been fueled in part by features such as fault-tolerance and a simple processing model. The amount of XML used in scientific, government, and enterprise data has grown substantially. and there are several high-level languages developed for Hadoop that can process semi-structured data like XML. ChuQL is a recently proposed extension to XQuery for processing XML natively using Hadoop. The current implementation of ChuQL leverages an existing main-memory XQuery processor and faces two challenges; intermediate XML values growing larger than memory and huge quantities of output files. We describe two ChuQL constructs to overcome these limitations: using an iterator to process XML value sequences, and partitioning the job output. We give experimental evidence to help evaluate the tradeoffs when using these advanced ChuQL features.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it