Generic meta model of cumulative fact table for non-sequential processes

Process mining refers to the set of techniques used to exploit the event logs of process-oriented information systems that manage and execute processes to gain hidden knowledge about an organization's actual business processes. The literature review noted that in the case of a non-sequential process, the structure of the cumulative fact table and the filling procedure do not allow all the data relating to an instance to be recorded. During the execution of the processes, loops are formed. Loops are explanatory variables for understanding process performance indicators such as cost, delay, and failure. We have proposed a meta-model for the cumulative fact table, allowing to take into account the filling procedure and the recording of all data related to an instance.