SOFTWARE DEVELOPMENT AND RESEARCH FOR MACHINE LEARNING-BASED STRUCTURAL ERRORS DETECTION IN BPMN MODELS
The most important tool for process management is business process modeling. Business process models allow to graphically represent the sequences of events, activities, and decision points that make up business processes. However, models that contain errors in depicting the business process structur...
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| Format: | Article |
| Language: | English |
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National Technical University Kharkiv Polytechnic Institute
2024-12-01
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| Series: | Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології |
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| Online Access: | http://samit.khpi.edu.ua/article/view/320149 |
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| author | Andrii Kopp Dmytro Orlovskyi Igor Gamayun Illia Sapozhnykov |
| author_facet | Andrii Kopp Dmytro Orlovskyi Igor Gamayun Illia Sapozhnykov |
| author_sort | Andrii Kopp |
| collection | DOAJ |
| description | The most important tool for process management is business process modeling. Business process models allow to graphically represent the sequences of events, activities, and decision points that make up business processes. However, models that contain errors in depicting the business process structure can lead to misunderstanding of a business process, errors in its execution, and associated expenses. Thus, the aim of this study is to ensure the comprehensibility of business process models by detecting structural errors in business process models and their subsequent correction. During the analysis of the Business Process Management (BPM) lifecycle, it was found that the created business process models do not have a stage of control for the presence of errors in them. Therefore, the paper analyzes and improves the BPM lifecycle using the proposed approach. In the improved BPM lifecycle, it is proposed to take into account the correctness validation stage of business process models using the developed software. The paper proposes to process created BPMN (Business Process Model and Notation) models as connected directed graphs. To detect errors in business process models, one of the Machine Learning methods, K-Nearest Neighbors, is chosen, which is a fairly simple and effective classification method. The study also includes the software design and development, its performance validation, and usage to solve the given problem. To analyze the obtained results, the confusion matrix was used and the corresponding quality metrics were calculated. The obtained results confirm the suitability of the developed software for detecting structural errors in business process models. This web application, which is based on the created classification model, allows all interested users to upload business process models in BPMN 2.0 format, view the uploaded models, and analyze them for structural errors. |
| format | Article |
| id | doaj-art-0aa5072ec61144769e94a965537ef3a9 |
| institution | Kabale University |
| issn | 2079-0023 2410-2857 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | National Technical University Kharkiv Polytechnic Institute |
| record_format | Article |
| series | Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології |
| spelling | doaj-art-0aa5072ec61144769e94a965537ef3a92025-01-08T14:40:15ZengNational Technical University Kharkiv Polytechnic InstituteВісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології2079-00232410-28572024-12-012 (12)465510.20998/2079-0023.2024.02.08358810SOFTWARE DEVELOPMENT AND RESEARCH FOR MACHINE LEARNING-BASED STRUCTURAL ERRORS DETECTION IN BPMN MODELSAndrii Kopp0https://orcid.org/0000-0002-3189-5623Dmytro Orlovskyi1https://orcid.org/0000-0002-8261-2988Igor Gamayun2https://orcid.org/0000-0003-2099-4658Illia Sapozhnykov3https://orcid.org/0009-0003-2802-1807National Technical University "Kharkiv Polytechnic Institute"National Technical University "Kharkiv Polytechnic Institute"National Technical University "Kharkiv Polytechnic Institute"National Technical University "Kharkiv Polytechnic Institute"The most important tool for process management is business process modeling. Business process models allow to graphically represent the sequences of events, activities, and decision points that make up business processes. However, models that contain errors in depicting the business process structure can lead to misunderstanding of a business process, errors in its execution, and associated expenses. Thus, the aim of this study is to ensure the comprehensibility of business process models by detecting structural errors in business process models and their subsequent correction. During the analysis of the Business Process Management (BPM) lifecycle, it was found that the created business process models do not have a stage of control for the presence of errors in them. Therefore, the paper analyzes and improves the BPM lifecycle using the proposed approach. In the improved BPM lifecycle, it is proposed to take into account the correctness validation stage of business process models using the developed software. The paper proposes to process created BPMN (Business Process Model and Notation) models as connected directed graphs. To detect errors in business process models, one of the Machine Learning methods, K-Nearest Neighbors, is chosen, which is a fairly simple and effective classification method. The study also includes the software design and development, its performance validation, and usage to solve the given problem. To analyze the obtained results, the confusion matrix was used and the corresponding quality metrics were calculated. The obtained results confirm the suitability of the developed software for detecting structural errors in business process models. This web application, which is based on the created classification model, allows all interested users to upload business process models in BPMN 2.0 format, view the uploaded models, and analyze them for structural errors.http://samit.khpi.edu.ua/article/view/320149business process modelsstructural errorsbpmn structural analysismachine learning |
| spellingShingle | Andrii Kopp Dmytro Orlovskyi Igor Gamayun Illia Sapozhnykov SOFTWARE DEVELOPMENT AND RESEARCH FOR MACHINE LEARNING-BASED STRUCTURAL ERRORS DETECTION IN BPMN MODELS Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології business process models structural errors bpmn structural analysis machine learning |
| title | SOFTWARE DEVELOPMENT AND RESEARCH FOR MACHINE LEARNING-BASED STRUCTURAL ERRORS DETECTION IN BPMN MODELS |
| title_full | SOFTWARE DEVELOPMENT AND RESEARCH FOR MACHINE LEARNING-BASED STRUCTURAL ERRORS DETECTION IN BPMN MODELS |
| title_fullStr | SOFTWARE DEVELOPMENT AND RESEARCH FOR MACHINE LEARNING-BASED STRUCTURAL ERRORS DETECTION IN BPMN MODELS |
| title_full_unstemmed | SOFTWARE DEVELOPMENT AND RESEARCH FOR MACHINE LEARNING-BASED STRUCTURAL ERRORS DETECTION IN BPMN MODELS |
| title_short | SOFTWARE DEVELOPMENT AND RESEARCH FOR MACHINE LEARNING-BASED STRUCTURAL ERRORS DETECTION IN BPMN MODELS |
| title_sort | software development and research for machine learning based structural errors detection in bpmn models |
| topic | business process models structural errors bpmn structural analysis machine learning |
| url | http://samit.khpi.edu.ua/article/view/320149 |
| work_keys_str_mv | AT andriikopp softwaredevelopmentandresearchformachinelearningbasedstructuralerrorsdetectioninbpmnmodels AT dmytroorlovskyi softwaredevelopmentandresearchformachinelearningbasedstructuralerrorsdetectioninbpmnmodels AT igorgamayun softwaredevelopmentandresearchformachinelearningbasedstructuralerrorsdetectioninbpmnmodels AT illiasapozhnykov softwaredevelopmentandresearchformachinelearningbasedstructuralerrorsdetectioninbpmnmodels |