Repairing Event Logs to Enhance the Performance of a Process Mining Model

Abstract

Organizations and companies are starving to improve their business processes to stay in competition. As we know that process mining is a young and emerging study that lasts among data mining and machine learning. The main goal of process mining is to obtain accurate information from the data; therefore, in recent years, it attracts the attention of many researchers, practitioners, and vendors. However, the purpose of enhancement is to extend or develop an existing process model by taking information from the actual process recorded in an event log. One type of enhancement of a process mining model is repair. It is common practice that due to logging errors in information systems or the presence of a special behavior process, they have the actual event logs with the noise. Hence, the event logs are traditionally thought to be defined as situation. Actually, when the logging is based on manual logging i.e., entering data in hospitals when patients are admitted for treatment while recording manually, events and timestamps are missing or recorded incorrectly. Our paper is based on theoretical and practical research work. The main purpose of our study is to use the knowledge gather from the process model, and give a technique to repair the missing events in a log. However, this technique gives us the analysis of incomplete logs. Our work is based on time and data perspectives. As our proposed approach allows us to repair the event log by using stochastic Petri net, alignment, and converting them into Bayesian analysis, which improves the performance of the process mining model. In the end, we evaluate our results by using the algorithms described in the alignment and generate synthetic/artificial data that are applied as a plug-in in a process mining framework ProM.