Towards Intelligent Technology for Error Detection and Quality Evaluation of Business Process Models
Year: 2023
Type: conference-paper
Abstract: Business process modeling is an essential technique of business process management, used to align business and information technology sides in an organization. Business process models are graphical diagrams used to capture, analyze, and improve organizational activities. High- quality business process models are used to detect inefficiencies in enterprise workflows and gather requirements for supportive software systems. However, poor business process models are less understandable, hardly maintainable, error-prone, and may lead to expenses and time losses caused by occurring errors. Hence, the continuous quality analysis of created business process models should be introduced as a part of the business process management lifecycle, necessary to detect and eliminate modeling errors. In this study, we propose the connectionist system based on reinforcement learning principles to take into account the occurrence of various modeling errors and their impact on the total quality estimations. The software tool is created to implement this intelligent system, perform experiments using a large collection of business process models, analyze, and discuss obtained results.
Authors: Andrii Kopp, Dmytro Orlovskyi
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W7166306992