A Colored Petri Net Executable Modeling Approach for a Data Flow Well-Structured BPMN Process Model
BPMN process models have been widely used in software designs. The BPMN process models are characterized by a static graph-oriented modeling language and a lack of analytical capabilities as well as dynamic behavior verification capabilities, which not only leads to inconsistencies in the semantics...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9857829/ |
| _version_ | 1811287005829529600 |
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| author | Fenglan Huang Feng Ni Jiang Liu Fan Yang Jiayi Zhu |
| author_facet | Fenglan Huang Feng Ni Jiang Liu Fan Yang Jiayi Zhu |
| author_sort | Fenglan Huang |
| collection | DOAJ |
| description | BPMN process models have been widely used in software designs. The BPMN process models are characterized by a static graph-oriented modeling language and a lack of analytical capabilities as well as dynamic behavior verification capabilities, which not only leads to inconsistencies in the semantics of the BPMN process models, but also leads to a lack of model error detection capabilities for the BPMN process models, which also hinders the correctness verification and error correction efforts of the models. In this study, we propose an executable modeling approach for CPN-based data flow well-structured BPMN (dw-BPMN) process models, and consider both control-flow and data-flow perspectives. First, we present a formal definition of the dw-BPMN process model, which is formally mapped into a CPN executable model in three steps: splitting, mapping and combining. Then, we discuss four types of data flow errors that can occur in the model: missing, lost, redundant, and inconsistent data error. To detect these four data flow errors, we propose a detection method based on the execution results of the CPN model. Subsequently, we propose correction strategies for these four data flow errors. Finally, a dw-BPMN process model of a robot’s temperature detection system for COVID-19 prevention and control in a kindergarten was used as an example to verify the validity of the method. |
| first_indexed | 2024-04-13T03:10:51Z |
| format | Article |
| id | doaj.art-9584a7c883cc4928a997c2e0a929aca2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T03:10:51Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9584a7c883cc4928a997c2e0a929aca22022-12-22T03:05:05ZengIEEEIEEE Access2169-35362022-01-0110866968670910.1109/ACCESS.2022.31989699857829A Colored Petri Net Executable Modeling Approach for a Data Flow Well-Structured BPMN Process ModelFenglan Huang0Feng Ni1https://orcid.org/0000-0003-1273-7838Jiang Liu2Fan Yang3Jiayi Zhu4Business School, University of Shanghai for Science and Technology, Shanghai, ChinaBusiness School, University of Shanghai for Science and Technology, Shanghai, ChinaBusiness School, University of Shanghai for Science and Technology, Shanghai, ChinaShanghai Sunshine City Kindergarten, Shanghai, ChinaBusiness School, University of Shanghai for Science and Technology, Shanghai, ChinaBPMN process models have been widely used in software designs. The BPMN process models are characterized by a static graph-oriented modeling language and a lack of analytical capabilities as well as dynamic behavior verification capabilities, which not only leads to inconsistencies in the semantics of the BPMN process models, but also leads to a lack of model error detection capabilities for the BPMN process models, which also hinders the correctness verification and error correction efforts of the models. In this study, we propose an executable modeling approach for CPN-based data flow well-structured BPMN (dw-BPMN) process models, and consider both control-flow and data-flow perspectives. First, we present a formal definition of the dw-BPMN process model, which is formally mapped into a CPN executable model in three steps: splitting, mapping and combining. Then, we discuss four types of data flow errors that can occur in the model: missing, lost, redundant, and inconsistent data error. To detect these four data flow errors, we propose a detection method based on the execution results of the CPN model. Subsequently, we propose correction strategies for these four data flow errors. Finally, a dw-BPMN process model of a robot’s temperature detection system for COVID-19 prevention and control in a kindergarten was used as an example to verify the validity of the method.https://ieeexplore.ieee.org/document/9857829/BPMNformal verificationCPNdata flowmodel transformation |
| spellingShingle | Fenglan Huang Feng Ni Jiang Liu Fan Yang Jiayi Zhu A Colored Petri Net Executable Modeling Approach for a Data Flow Well-Structured BPMN Process Model IEEE Access BPMN formal verification CPN data flow model transformation |
| title | A Colored Petri Net Executable Modeling Approach for a Data Flow Well-Structured BPMN Process Model |
| title_full | A Colored Petri Net Executable Modeling Approach for a Data Flow Well-Structured BPMN Process Model |
| title_fullStr | A Colored Petri Net Executable Modeling Approach for a Data Flow Well-Structured BPMN Process Model |
| title_full_unstemmed | A Colored Petri Net Executable Modeling Approach for a Data Flow Well-Structured BPMN Process Model |
| title_short | A Colored Petri Net Executable Modeling Approach for a Data Flow Well-Structured BPMN Process Model |
| title_sort | colored petri net executable modeling approach for a data flow well structured bpmn process model |
| topic | BPMN formal verification CPN data flow model transformation |
| url | https://ieeexplore.ieee.org/document/9857829/ |
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