Using Web services choreography to support an extensible and flexible system development process
Bibliographic record
Abstract
Systems Engineering (SE) controls a complex environment consisting of various collaborative subsystems. Each subsystem demands different kind of requirements and follows a specific strategy for its development process. Unifying and harmonizing the development process of all collaborative subsystems towards achieving the ultimate integrated system is one of the main challenges of SE. This work introduces a new approach towards having a generic SE unified process applicable to various environments. We suggest a service-oriented framework for SE process implemented using Web Services, and describe the process scenario in a machine-friendly abstract layer over the Development Process. This description layer choreographs collaborative subsystems and is implemented by a Web Services Choreography Description Language (WSCDL). It also covers Interface Management concerns of SE. In such an environment, as long as all services follow a unique framework for the SE process such as the one specified by the International Council on Systems Engineering (INCOSE), each phase of the process would then be an anonymous service implemented by a different vendor. As the result, an organization could easily customize its own specific development environment by editing this choreography layer according to its specific development policies, and then tailor its own desired development environment by choosing and integrating various services available on the Web. Source: Masters Abstracts International, Volume: 45-01, page: 0351. Thesis (M.Sc.)--University of Windsor (Canada), 2006.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.007 |
| 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".