Representing UML snowflake diagram from integrating XML data using XML schema
Bibliographic record
Abstract
We present a UML conceptual-level integration framework for supporting OLAP operations on diverse XML data sources. Our integration framework takes advantage of the constructs and relationships existing within XML schemas. Two processes are involved in our integration framework. The first one is to convert XML schemas into UML class diagrams. The second is to build a multidimensional model from the UML class diagrams. This paper focuses on the issues in the second process. We describe how to represent a multidimensional model using a UML snowflake diagram and how to represent the UML snow/lake diagram using XML schemas. Our advanced XML schema-based representation of a multidimensional model is used as metadata in our prototype system for processing OLAP queries. It elegantly holds the information within a UML snowflake diagram such as the retrieval path through which we can obtain the participating XML data sources for OLAP operations. To the best of our knowledge, we are the first people to apply the advantages of XML Schemas during integrating XML data sources for virtual OLAP and to propose an XML schema-based method for representing a UML snowflake diagram that integrates heterogeneous XML data sources.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".