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The impact of biased sampling of event logs on the performance of process discovery
2021
Computing
AbstractWith Process discovery algorithms, we discover process models based on event data, captured during the execution of business processes. The process discovery algorithms tend to use the whole event data. When dealing with large event data, it is no longer feasible to use standard hardware in a limited time. A straightforward approach to overcome this problem is to down-size the data utilizing a random sampling method. However, little research has been conducted on selecting the right
doi:10.1007/s00607-021-00910-4
fatcat:gprk7efzazfgrkicnrhskrrguq