Sibling-First Data Organization: For Efficient XML Data Processing
Bibliographic record
Abstract
XML is becoming one of the most important structures for data exchange. Despite having many advantages, XML structure imposes several major obstacles to large document processing. Incompatibility between the linear nature of the current algorithms such as caching and prefetch used in operating systems and databases, and the non-linear structure of XML data makes XML processing more costly. In addition to verbosity, parsing depth-first (DF) structure of XML documents is a significant overhead to processing applications, including search engines. Recent research on XML query processing has learned that sibling clustering can improve performance significantly. However, the existing methods are limited in several aspects including in processing very large documents. In this research, a better data organization has been developed for native XML databases, named sibling-first (SF), that significantly improves the performance in large data processing. SF uses an embedded index for fast access to child nodes. It also compresses documents by eliminating extra data from the original DF format. The converted SF documents can be processed for XPath query purposes without being parsed. The SF storage has been implemented in virtual memory as well as a format on disk. Experimental results with real data have shown that significantly higher performance can be achieved when XPath queries are conducted on very large SF 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.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.008 | 0.008 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".