QoS-Aware Middleware for Web Services Composition - A Qualitative Approach
Bibliographic record
Abstract
One of the benefits of web services is their ability to participate in a web services composition process. Therefore, an end-to-end QoS infrastructure should be established. Work conducted in this domain is mainly focused on functional QoS requirements such as service response time, delay, cost, etc. In this paper, we target QoS from the prespective of data freshness and accuracy. Therefore, we propose the usage of the WS-Notification specification as a base medium capable of sensing and routing any information change at the level of web services using a publish-subscribe mechanism. We then propose an algorithm that is capable of identifying the point of information change within the context of multiple web services composition scenario. This is then followed with an appropriate re-computation of a subset of the pre-established, global service execution plan. Our contributions are three fold: first we highlight the importance of qualifyable QoS aspect related to the issue of web services composition and monitoring, second we describe an algorithm capable of capturing and reflecting the state of web services involved in the integration process, and finally we illustrate the usage of WS-Notification to aid in building such systems.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".