ENHANCING BUSINESS COLLABORATIONS WITH CLIENT-ORIENTED PROCESS CONTROL
Bibliographic record
Abstract
Nowadays, business collaborations have to be highly dynamic and flexible to allow companies to operate efficiently and effectively in complex and volatile markets. To increase the business agility of service consumers, it is fundamental that service providers enhance the visibility of parts of their collaborative processes. Service providers are required to release both the process structures of the services offered and their status during execution. To further increase the flexibility of business collaborations, certain control over the process execution has to be offered to service consumers. In this paper, we present a framework for the support of process control in cross-organizational settings. We specify the control primitives that can be used to exert control on activities and processes before, during and after their executions. These primitives empower service consumers to postpone activity and process executions, bypass minor activities, repeat their executions, etc. We describe an approach to the support of these control primitives by service providers. We demonstrate the application of our framework with a case study from the healthcare domain. A proof-of-concept prototype implementation based on Web service technology is presented.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".