Integration of Management of Quality of Web Services in Service Oriented Architecture
Bibliographic record
Abstract
With the proliferation of Web services and their wide adoption as a novel technology for business-to-business interactions on the Web, Quality of Web Services (QoWS) management has witnessed considerable interest in recent years. Most of the existing works regarding this issue do not provide support for the overall QoWS management operations and are very often limited to a subset of these operations. Some of these works propose QoWS solutions for only basic web services while others propose solutions for composite web services. In this chapter, we propose to extend the Service Oriented Architecture (SOA) with a framework for QoWS management in which services may be basic or composite Web services. The framework uses a layered approach to provide support for the most common QoWS management operations, which include QoWS specification, QoWS verification, QoWS negotiation, and QoWS monitoring. These operations are supported at both the design and the development phases of Web services.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.032 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".