On Enhancing the Quality of Business Process Model’s Activities Labels

Abstract

Business process models describe their operations, events, and control flows through graphical illustrations to increase the knowledge and awareness of business processes. Large corporations use models to document and design business processes. With the growing number of business process models and trained modelers, modeling initiatives demand quality assurance. Nevertheless, checking the quality of the process model, especially its activity labels, is a challenge. In labels, synonymy, vagueness, homonymy, incorrect labeling, as well as different modeling styles result in ambiguity, uncertainty, and misunderstandings. Quality of activity labels rely on precise and fitful words which are according to the domain process models taking quality parameters under consideration. The problem arises when the activity labels are too short and provide limited information and words facing the zero-derivation problem. For this purpose, algorithms have been deployed which will recognize, identify and check the labeling styles of a process model. Activity labels has been extracted automatically. Further, NLP techniques like WordNet has been used for the analysis of activity labels. In this study, the quality of textual labels in activities of a process models is addressed. Activity labels has been analyzed using a collection of business process models based on medical chronic diseases. Results obtained by deployment of algorithms on automatically extracted labels confirms the applicability and accuracy of proposed techniques.