Modeling of Web Services using Reaction Rules
Bibliographic record
Abstract
The development process of Web services needs to focus on the modeling of business processes rather than on low-level implementation details of Web services, and yet it also needs to incorporate the support for frequent business changes. This chapter presents the UML-based Rule Language (URML) and REWERSE Rule Markup Language (R2ML), which use reaction rules (also known as Event-Condition- Action rules) for modeling Web services in terms of message exchange patterns. Web services that are being modeled in this way can easily be integrated in the wider context of modeling orchestration and choreography. In order to achieve proposed solution, we have developed a plug-in for the Fujaba UML tool (so called Strelka) and a number of model transformations for round-trip engineering between Web services and reaction rules. Also, the paper presents mappings of models of Web services with reaction rules into the Drools rule language, thus enabling the run time execution semantics for our rule-based models.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".