A formal description framework and a matchmaking technique for web service composition
Bibliographic record
Abstract
Purpose A web service is a software system designed to support interoperable machine‐to‐machine or application‐to‐application interactions over networks. Descriptions enable web services to be discovered, used by other web services, and composed into new web services. Web service composition is a mechanism for creating new web services by reusing existing ones. In order to compose a web service, the right primitive services have to be discovered. A matchmaking technique enables discovering these services. Web services have functional, non‐functional, behavioral, and semantic characteristics. These four aspects of web services provide different key information about the service; therefore they have to be considered for description, matching, and composition. The purpose of this paper is to propose a formal description framework and a formal matchmaking technique that allows describing and discovering web services by considering their four characteristics. Design/methodology/approach In this paper, the description framework combines two existing languages for functional, semantic, and behavioral description, along with a simple and new language for non‐functional description. Findings A case study is used to illustrate the description framework and the matchmaking technique. The implementation and performance evaluation of the matchmaking technique is presented. The framework formalizes and integrates the languages in a common semantic domain in order to match and manipulate the different aspects together and formally. Isabelle is used by the matchmaking technique for discovering the partially and fully matched services. Originality/value The contribution of this paper lies in the new description framework and the new matchmaking technique.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".