Programming Language Design for Event-Driven Service Composition
Bibliographic record
Abstract
To adapt to rapidly changing market conditions and increase the return of investment, today's IT solutions usually combine service-oriented architecture (SOA) and event-driven architecture (EDA) that support reusability, flexibility, and responsiveness of business processes. Programming languages for development of event-driven service compositions face several main challenges. First, a language should be based on standard service composition languages to be compatible with SOA-enabling technologies. Second, a language should enable seamless integration of services into event-driven workflows. Third, to overcome a knowledge divide, language should enable seamless cooperation between application developers with different skills and knowledge.Since WS-BPEL is widely accepted as standard executable language in SOA, we extended WS-BPEL with support for event-driven workflow coordination. We designed event-handling mechanisms as special-purpose Coopetition services and augmented WS-BPEL with primitives for their invocation. Coopetition services augment SOA with fundamental EDA characteristics: decoupled interactions, many-to-many communication, publish/subscribe messaging, event triggering, and asynchronous operations. To make the application development familiar to wide community of developers, we designed an application-level end-user language on top of WS-BPEL whose primitives for invocation of regular Web services and Coopetition services resemble the constructs of typical scripting and coordination language.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".