Pattern-Oriented Approach for Enterprise Architecture: TOGAF Framework
Bibliographic record
Abstract
Design pattern suggests that developers must be able to reuse proven solutions emerging from the best design practices to solve common design problems while composing patterns to create reusable designs that can be mapped to different types of enterprise frameworks and architectures such as The Open Group Architecture Framework (TOGAF). Without this, business analysts, designers and developers are not properly applying design solutions or take full benefit of the power of patterns as reuse blocks, resulting in poor performance, poor scalability, and poor usability. Furthermore, these professionals may “reinvent the wheel” when attempting to implement the same design for different types of architectures of TOGAF framework. In this paper, we introduce different categories of design patterns as a vehicle for capturing and reusing good analyses, designs and implementation applied to TOGAF framework while detailing a motivating exemplar on how design patterns can be composed to create generic types of architectures of TOGAF framework. Then, we discuss why patterns are a suitable for developing and documenting various architectures including enterprise architectures as TOGAF.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".