Constraint Adaptation in Web Service Composition
Bibliographic record
Abstract
Service constraints are usage restrictions on service features that are imposed by service providers. Such constraints need to be verified prior to the execution of a service in order to ensure correct service execution. In the case of composite services, the set of applicable constraints is derived from the service constraints defined over the individual service components that are part of the service plan. During the execution of a composite service, a constraint-aware composite service execution model can be used to manage and eventually operationally verify the service constraints prior to the corresponding service's execution. In addition to service constraints, other constraints might be imposed to put externally-defined restrictions on composite services. Such externally-defined restrictions are likely to be defined and become or cease to be applicable after the composite service has been assemble and deployed. Such a situation requires adaptation of the plan to a set of externally-defined constraints. Current web service composition adaptation approaches only focus on adaptation to failure in functional capabilities or Quality of Service (QoS) properties which can be dealt with re-construction of the composite service. However, we argue that adaptation to external constraints does not necessarily require changes in the plan of a composite service. In this paper, we define a constraint-based composite service model that not only considers service constraints, but also adapts composite plans according to new constraints that might add new restriction to the composite service at run time. A publicly available test set generator is used to compare the proposed solution with other existing service adaptation solutions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".