Integrating XML data sources using approximate joins
Bibliographic record
Abstract
XML is widely recognized as the data interchange standard of tomorrow because of its ability to represent data from a variety of sources. Hence, XML is likely to be the format through which data from multiple sources is integrated. In this article, we study the problem of integrating XML data sources through correlations realized as join operations. A challenging aspect of this operation is the XML document structure. Two documents might convey approximately or exactly the same information but may be quite different in structure. Consequently, an approximate match in structure, in addition to content, has to be folded into the join operation. We quantify an approximate match in structure and content for pairs of XML documents using well defined notions of distance. We show how notions of distance that have metric properties can be incorporated in a framework for joins between XML data sources and introduce the idea of reference sets to facilitate this operation. Intuitively, a reference set consists of data elements used to project the data space. We characterize what constitutes a good choice of a reference set, and we propose sampling-based algorithms to identify them. We then instantiate our join framework using the tree edit distance between a pair of trees. We next turn our attention to utilizing well known index structures to improve the performance of approximate XML join operations. We present a methodology enabling adaptation of index structures for this problem, and we instantiate it in terms of the R-tree. We demonstrate the practical utility of our solutions using large collections of real and synthetic XML data sets, varying parameters of interest, and highlighting the performance benefits of our approach.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".