Business Process Integration by Using General Rule Markup Language
Bibliographic record
Abstract
A business process usually includes multiple busi- ness partners that use systems with their business log- ics represented in different rule (or policy) languages. The integration of business processes is a major goal followed by business enterprises and involves the in- terchange of business rules and policies between part- ners. However, the variety of business rules employed by the partners presents a significant burden to the integration of collaborating business systems. In this paper, we propose a solution that enables translating rules and policies defined in different rule languages into a single general rule language (REWERSE I1 Rule Markup Language - R2ML) and processing them in a uniform manner. This provides a unified view of different partners' business rules in an integration process, while permitting the partners to continue to leverage their own business rules without changing their technologies. We show how the concepts of the KAoS policy language can be transformed to R2ML and then from R2ML to the other rule (or policy) lan- guages.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".