A Graph-Based Framework for Composition of Stateless Web Services
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Bibliographic record
Abstract
During the past few years, Web services composition has been one of the ongoing research activities in the software engineering area. It is simply defined as finding a composition of available Web services that provides a requested behavior. In this paper, we study this problem for stateless Web services; i.e., Web services with a two-step workflow: receiving some inputs and returning some outputs as the result. Using a graph model we represent the behavior of available Web services in terms of their input-output information, as well as semantic information about the Web data. We also introduce a process algebra to specify the behavior of composite Web services based on the behavior of simpler ones. Using the graph and the process algebra, we explain how to find useful Web services for a request and how to compose them to obtain the expected behavior. We discuss the complexity of this approach and show that although it is a naturally complex process, by applying some simplifications, a reasonable overall complexity can be achieved
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it