| Title |
Explainable Artificial Intelligence based Process Mining Analysis Automatio |
| Authors |
정찬일(Chanyil Jung) ; 이후진(Hoojin Lee) |
| DOI |
https://doi.org/10.5573/ieie.2019.56.11.45 |
| Keywords |
Process mining ; Process analysis automation ; Machine learning ; eXplainable AI ; |
| Abstract |
Process mining is one of the business management techniques for business innovation. While it automates the creation of execution process models in the information system log, it still relies on the analyst's relevant experience and knowledge of the business domain in analyzing the process content and the root cause of the problem. Since most of the derived process models are very complicated, an accurate analysis is impossible due to the limitations of human cognition ability, and most of the process mining algorithms go through an abstraction process. There is a possibility that distortions may occur in this process, or important issues may be missed. Hence, in this study, we attempted to predict the process results by using the process prediction model incorporating a LSTM algorithm of machine learning, and then to apply an explainable artificial intelligence(XAI) technique. Thus, this study has drastically reduced the reliance on the human intuition and knowledge in the root cause analysis of process mining, enabling the quantitative and rapid analysis. |