Consented consumer-centric non-functional property description and composition for SOA-based applications
Bibliographic record
Abstract
Non-functional properties (NFPs) play an important role in the service-oriented architecture (SOA). Consumer-centric NFPs are the NFPs that should be included in a service description to help service consumers decide whether a given service suits their needs. They can hence be used to enable NFP-based service selection and composition. However, nowadays, NFPs are often simply not advertised or are described in ad-hoc proprietary ways. Three important factors impede on the proper handling of NFPs in service descriptions: 1) the neglect of consumer perspectives in SOA; 2) the lack of adequate descriptive mechanisms for a number of NFPs; 3) a good understanding of NFP composability. This paper contributes a concrete syntax for an externally consented catalogue of 17 consumer-centric NFPs, together with composition algorithms that can be effectively used for defining, selecting, and composing services for NFP-aware SOA-based application designs. A realistic use case is used to illustrate the NFP composition algorithms. The NFP catalogue is also validated through its proof-of-concept integration with a mainstream technology: Web Service Description Language (WSDL).
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".