Transforming XML documents as schemas evolve
Bibliographic record
Abstract
Database systems often use XML schema to describe the format of valid XML documents. Usually, this format is determined when the system is designed. Sometimes, in an already functioning system, a need arises to change the XML schemas. In such a situation, the system has to transform the old XML documents so that they conform to the new format and that as little information as possible is lost in the process. This process is called schema evolution . We have implemented an XML schema transformation toolkit within IBM Master Data Management Server (MDM). MDM uses XML documents to describe products that an enterprise may be offering to its clients. In this work we focus on evolving schemas rather than on integrating separate or heterogeneous data sources. Our solution includes an extendible schema matching algorithm that was designed with evolving XML schemas in mind and takes advantage of hierarchical structure of XML. It also includes a data transformation and migration method appropriate for environments where migration is performed in an abstraction layer above the DBMS. Finally, we describe a novel way of extending an XSLT editor with an XSLT visualization feature to allow the user's input and evaluation of the transformation.
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 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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".