Validation of enterprise architecture through colored Petri nets
Bibliographic record
Abstract
Enterprise architecture procedure contains some instructions for conversion of enterprise architecture from the current state to the desirable state. This procedure generally contains 3 phases each of which is the basis and prerequisite of the next phase. These phases are: Strategic information technology planning, enterprise architecture planning and enterprise architecture execution. As each phase is a prerequisite of the next one, any fault in each phase causes bigger faults in final results. Therefore, each phase should be double checked to make sure no fault has occurred. The second phase can greatly influence the final results. Therefore the preparation of an executable enterprise architecture model and checking it with functional and non-functional requirements can prevent many faults and lead to execution of a perfect model of the enterprise. The primary objective of this research is to check the accuracy of EA behavior in achieving an appropriate architecture. In this research, official models have been used to propose a solution to transform the products of C4ISR framework to executable Petri nets. Finally, a method is proposed to check the accuracy of the mentioned model. The proposed solution makes the EA semi-automatically check the correctness of the enterprise architecture behavior and increase its accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".