AXECHOP: A Grammar-based Compressor for XML
Bibliographic record
Abstract
Summary form only given. XML is gaining widespread acceptance as a standard for storing and transmitting structured data. One of the drawbacks of XML is that it is quite verbose: an XML representation of a set of data can easily be ten times as large as a more economical representation of the data. To overcome this limitation, we present a compression scheme tailored specifically to XML named AXECHOP. The compression strategy used in AXECHOP begins by dividing the source XML document into structural and data segments. The former is represented using a byte tokenization scheme that preserves the original structure of the document (i.e. it maintains the proper nesting and ordering of elements, attributes, and data values). The MPM compression algorithm is used to generate a context-free grammar capable of deriving this original structure, and the grammar is passed through an adaptive arithmetic coder before being written to the compressed file. The document's data is organized into a series of containers (where container membership is determined by the identity of the XML element or attribute that encloses the data) and then the Burrows-Wheeler transform (BWT) is applied to the contents of each dictionary, with the results being appended to the compressed file.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".