Assessing the Applicability of Use Case Maps for Business Process and Workflow Description
Bibliographic record
Abstract
Use Case Maps (UCMs) have already been used to describe business processes and workflows at a high level of abstraction. The semantics of UCMs, however, require further clarification and enhancement. An initial assessment based on 27 workflow and communication patterns (a) highlighted some of the semantic variation points of UCMs, (b) introduced small extensions to the UCM language in order to more precisely define scenarios, high-level business processes, and workflows, and (c) compared UCMs with other business process and workflow languages. This short paper summarizes the continuation of the assessment with a larger set of workflow patterns recently made available. The assessment concludes that the UCM notation including the proposed extensions is a competitive language to describe high-level business processes and workflows, while providing additional benefits over the other languages.
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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 | 0.093 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".