Bibliographic record
Abstract
Web services adoption is a major advance in the development of interoperable information systems. In particular, the composition of services can meet the needs increasingly complex of user, by a combination of web services within a single business process. However, despite this widespread adoption of Web services, many obstacles prevent their reconciliation in the composition, or may occur within a BPEL process in a state change, the context for example. ASWSCC Method (Adaptation of Semantic Web Service Composition to Context) is an implementation of a theoretical model made in our an earlier work. It focuses on composition process adaptation to use context (preferences, user type and its environment as the device used, location, access mode and many others). This context and request service matching should be taken into account while composing new services. Our goal is to develop a model which ensures, on the one hand, web services matching during composition process by using domain ontology as lexical database WordNet, its purpose is to identify, classify and relate in different ways semantic content and lexical language. On the other hand, this model allows management and taking into account the context that makes composition process adaptable to different instances of use context, which may change during the same session. For this reason, we are interested to capture and manage the context and its impact on basic services and composition process at once. Changes can affect the context of web services during their executions and the need to adapt their dynamically becomes increasingly crucial. From here comes the need for a coherent solution to adapt web services context. We exploit the benefits of aspect weaving tool in this approach to inject aspects of web services to adapt them to change of context. Keywords-Context definition and management; adaptation; web services composition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".