Transformation from CIM to PIM Using Patterns and Archetypes
Bibliographic record
Abstract
Model transformations form a key part of MDA (model-driven architecture). Most of the studies deal with the transformations from PIM (platform-independent model) to PSM (platform-specific model) and PSM to Code, but very few deal with the transformation from CIM (computation-independent model) to PIM. This last transformation usually depends on business analysts' and software architects' experience and creativity. This paper proposes a disciplined approach to transform a CIM into a PIM. It first uses UML2 activity diagrams to model the business processes up to the users' tasks. The activity diagrams are then detailed to specify the system requirements. The system components are directly deduced from the requirement model elements. Finally, a set of business archetypes helps detail the system components to yield the PIM. The same approach applies equally to CIM and PIM built to model inter-enterprise processes and systems. A case study illustrates our approach. It demonstrates how it reinforces the components traceability and reusability and how it globally improves the modeler's efficiency. Furthermore, the use of the activity diagrams, as a single technique to build business process and requirement models, is an important facilitator which prepares our further work to automate this 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".