A FRAMEWORK FOR ADAPTIVE AND DYNAMIC COMPOSITION OF WEB SERVICES
Bibliographic record
Abstract
The modularity of web services has left an open problem in composition, a scenario that involves an amalgamation of two or more web services to fulfill a request that no one web service is able to provide. This paper presents a framework for adaptive and dynamic composition of web services, enabling web services to be discovered either statically or dynamically by utilizing a semantic ontology to describe web services and their methods. This novel approach gives greater control on how web services are dynamically discovered by allowing the application developer to specify how matches are made, which goes beyond the present techniques of semantically matching inputs and outputs along with classification taxonomies. We utilize the Composite Capabilities/Preferences Profiles (CC/PP) to adapt the interface and content to be compatible with virtually any device. A proof of concept implementation has been constructed that enables users of any device to dynamically discover context-based services that will be dynamically composed to satisfy a user's request. In addition, we have designed and implemented a UDDI-like registry to support context-based adaptive composition of web services. Existing web services can be easily adapted and new web services can be effortlessly deployed.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".