Using Design Guidelines to Improve Data Warehouse Logical Design.
Bibliographic record
Abstract
Data Warehouse-(DW) logical design often start with a conceptual schema and then generates relational structures. Applying this approach implies to cope with two main aspects: (i) -mapping the conceptual model structures to the logical model ones, and (ii) -taking into account implementation issues, which are not considered in the conceptual schema. This paper addresses this second aspect and presents a formalism that allows the DW designer to specify design guidelines which express design strategies related with implementation requirements. Through these guidelines the designer states high level manners to cope with different design problems, for example: managing complex and big dimensions, dimension versioning, different user profiles accessing to different attributes, high summarized data, horizontal partitions of historical data, generic dimensionality and non-additive measures. This work is part of a DW logical design environment, where the design guidelines are specified through a graphical editor and then automatically processed in order to build the logical schema.
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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 | 0.096 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".