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. e 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 de ned 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. e 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/arti cial data that are applied as a plug-in in a process mining framework ProM.
