Montasser, Reem KadryHelal, Iman M. A2023-09-282023-09-282023-0710.1109/IMSA58542.2023.10217621http://repository.msa.edu.eg/xmlui/handle/123456789/5732Process discovery algorithms incorporating domain knowledge can have varying levels of user involvement. It ranges from fully automated algorithms to interactive approaches where the user makes critical decisions about the process model. Designing domain knowledge using process discovery techniques faces various challenges. These challenges could cause some issues with existing approaches. Acquiring domain knowledge with domain experts, integrating domain knowledge with process data, scalability to handle large complex data sets, and ensuring data quality are examples of these challenges. In this survey, we assess recent work with varying levels of automation in process discovery to enhance the analysis and understanding of business processes within an organization. Current work can be classified into two categories: fully automated or semi-automated process discovery. We conclude that semi-automated process discovery gives a better opportunity for involving users. Also, the use of deep learning algorithms in automation gives better performance than machine learning algorithms.enAutomated process discovery; data exploration; deep learning; event logs; machine learning (ML); process mining; user interactionArticle10.1109/IMSA58542.2023.10217621