2024 № 2 Системний аналіз, управління та інформаційні технології
Постійне посилання колекціїhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/84913
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Документ Software development and research for machine learning-based structural errors detection in BPMN models(Національний технічний університет "Харківський політехнічний інститут", 2024) Kopp, Andrii Mykhailovych; Orlovskyi, Dmytro Leonidovych; Gamayun, Igor Petrovych; Sapozhnykov, Illia VitaliiovychThe 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.