An ontological account of flow-control components in BPMN process models
Bibliographic record
Abstract
The Business Process Model and Notation (BPMN) has been widely adopted in the recent years as one of the standard languages for visual description of business processes. BPMN however does not include a formal semantics, which is required for formal verification and validation of behaviors of BPMN models.Towards bridging this gap using first-order logic, we in this paper present an ontological/formal account of flow-control components in BPMN, using Situation Calculus and Petri nets. More precisely, we use SCOPE (Situation Calculus Ontology of PEtri nets), developed from our previous work, to formally describe flow-control related basic components (i.e., events, tasks, and gateways) in BPMN as SCOPE-based procedures. These components are first mapped from BPMN onto Petri nets.Our approach differs from other major approaches for assigning semantics to BPMN (e.g., the ones applying communicating sequential processes, or abstract state machines) in the following aspects. Firstly, the approach supports direct application of automated theorem proving for checking theory consistency or verifying dynamical properties of systems. Secondly, it defines concepts through aggregation of more basic concepts in a hierarchical way thus the adoptability and extensibility of the models are presumably high. Thirdly, Petri-net-based implementation is completely encapsulated such that interfaces between the system and its users are defined completely within a BPMN context. Finally, the approach can easily further adopt the concept of time.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".