An architecture for providing context in WS-BPEL processes
Bibliographic record
Abstract
Web Service Business Process Execution Language (WS-BPEL) business processes are increasingly used by organizations to automate their business activities. As the pace of change in an organization increases, these processes will be required to be more flexible; to do so they will have to account for an increasing amount of changing environment state, or context. This poses significant challenges for WS-BPEL programmers, who have to source, track, and update context from multiple entities in addition to implementing and maintaining core business logic. In this paper we present a solution to this problem based on the definition and use of context variables. We describe how context variables can be constructed using the WS-BPEL language extension mechanism, and then outline an architecture for representing, sourcing, and propagating context in a web-services environment using existing web-services standards and frameworks. We also propose additional WS-BPEL language enhancements that will increase the utility of context variables and offer WS-BPEL programmers new ways of interacting with environment state. We have implemented a prototype realizing our approach and present a purchase-and-shipping scenario as an example of its use.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".