Computing Refined Ordering Relations with Uncertainty for Acyclic Process Models
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Bibliographic record
Abstract
Since the behavior is the essential characteristic of business process models, and ordering relations between execution of tasks can be used to describe the behavior of process models, we need to compute the ordering relations between tasks in process models. This computation can be used for compliance checking and querying process models based on behavior. There are three basic types of ordering relations between two events in a concurrent system, i.e., causal, conflict, and concurrency. In this paper, we refine the causal and concurrency relations with uncertainty according to whether one task is always executed with the other task in the same instance. To compute the refined ordering relations with uncertainty efficiently, we propose some rules for adjacent tasks and some transitive laws for nonadjacent tasks together with their proofs. Based on these rules and laws, we propose an algorithm to compute the refined ordering relations for acyclic process models based on unfolding technology. The algorithm has a biquadrate time to the size of complete prefix unfolding of the original model.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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