FIX: feature-based indexing technique for XML documents
Bibliographic record
Abstract
In this paper, we study the problem of indexing an XML database. Existing XML indexing techniques focus on clustering methods based on the combinatorial structural properties of an XML document. These techniques cluster tree nodes into an index tree or graph based on their similarities in ancestor-descendant or sibling relationships. Index look-up then amounts to pattern matching on the clustered tree or graph. In this paper, we propose a feature-based indexing technique, called FIX, based on the spectral graph theory. The basic idea is that for each twig pattern in a collection of XML documents, we calculate a vector of features based on its structural properties. These features are used as a key for the patterns and stored in a B-tree or a multidimensional index tree. Given an XPath query, its feature vector is first calculated and looked up in the index. Then a further refinement phase is performed to fetch the final results. We experimentally study the indexing technique over two scenarios: a large collection of relatively smaller documents, and a single large document. Our experiments show that FIX provides great pruning power and could gain an order of magnitude performance improvement for many XPath queries over existing evaluation techniques.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".