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Record W2169976911 · doi:10.1109/sysose.2006.1652313

Using W3C Web Services Choreography Description Language to Define a Generic SE Development Process

2006· article· en· W2169976911 on OpenAlexaff
Ziad Kobti, Samin Emami

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsChoreographyComputer scienceProcess (computing)Software engineeringWeb serviceService (business)Business Process Execution LanguageSystems engineeringService-oriented architectureWorld Wide WebEngineeringProgramming language

Abstract

fetched live from OpenAlex

Systems engineering (SE) controls a complex environment consisting of various subsystems. Each subsystem demands different kind of requirements and follows specific strategy for its development process. Unifying and harmonizing the development process of all collaborative subsystems towards achieving the ultimate integrated system is the main challenge of systems engineering. This work introduces a new approach towards having a generic unified process applicable to various environments. We suggest a service-oriented framework implemented by Web services for SE process, and define a dynamic abstract layer over the process focused on choreography amongst collaborative subsystems. This layer, which is implemented by a Web services choreography description language, also covers interface management concerns of systems engineering. In such an environment, as long as all services follow a unique protocol like the International Council on Systems Engineering (INCOSE) SE process, each phase of the process could be an anonymous service implemented by a different organization.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0020.003
Scholarly communication0.0070.006
Open science0.0020.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.004

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.

Opus teacher head0.018
GPT teacher head0.252
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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