Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Workflow Management Systems (WfMS) that help the design and deployment of automated business processes as well as aid their execution and monitoring continue to evolve. Many WfMS use Workflow Patterns as their basic modeling constructs; however, the absence of verification facilities in most WfMS causes the resulting implementation to be at risk of undesirable runtime executions. Model Checking can facilitate the verification of workflow models, provided that we can conveniently implement the workflow model and provide the resources to handle the space requirement of the model. DiVinE is a distributed and parallel Model Checker that can effectively handle the well-known state explosion problem of this domain. In this paper, we present a translation of a collection of established Workflow Patterns into DVE, the input specification language of DiVinE. Thus, by assembling the corresponding DVE translated patterns into a whole model, we can verify properties of workflow models. We discuss the difficulties we have experienced with this approach and explain how that led to the development of an automatic translator tool from YAWL to DVE. We present two case studies and some ongoing work in our research group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it