Name
Abstract | ||
|---|---|---|
Petri nets are suitable for modeling and analysis of business processes. However, the lack of data concepts often makes Petri-net-based models excessively large and difficult to analyze especially when process logic is sensitive to changes of process attribute values. Emergency treatment processes are a typical example of this situation. To solve the aforementioned problems, this paper proposes a new hierarchical Petri net model for modeling and verification of emergency treatment processes. The hierarchical Petri net model includes three separate but closely related models, i.e., a business process logic net, a business process semantic net, and a set of case models. Business process logic nets are used to model the task dependencies disregarding semantic information. Business process semantic nets introduce data concepts to business process logic nets to model semantic information such as process attributes or conditions of sequence flows. Case models are used to model the practical routes of specific business instances. Based on the three models, a formal verification algorithm of an emergency treatment process is presented. Finally, the hierarchical modeling and verification methods are validated by an emergency treatment process of highways under snow/ice weather conditions. ? 2013 IEEE. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TSMC.2013.2242465 | IEEE T. Systems, Man, and Cybernetics: Systems |
Keywords | Field | DocType |
Semantics,Business,Synchronization,Analytical models,Ice,Snow,Petri nets | Artifact-centric business process model,Petri net,Computer science,Knowledge management,Artificial intelligence,Business process modeling,Software engineering,Business process,Process modeling,Process architecture,Business rule,Machine learning,Business Process Model and Notation | Journal |
Volume | Issue | ISSN |
44 | 2 | 2168-2216 |
Citations | PageRank | References |
5 | 0.45 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Faming Lu | 1 | 32 | 4.69 |
| Qingtian Zeng | 2 | 242 | 43.67 |
| Yunxia Bao | 3 | 10 | 2.32 |
| Hua Duan | 4 | 110 | 19.58 |