  The capability of process mining techniques in providing extensive knowledge
and insights into business processes has been widely acknowledged. Process
mining techniques support discovering process models as well as analyzing
process performance and bottlenecks in the past executions of processes.
However, process mining tends to be "backward-looking" rather than
"forward-looking" techniques like simulation. For example, process improvement
also requires "what-if" analyses. In this paper, we present a Python library
that uses an event log to directly generate a simulated event log, with
additional options for end-users to specify the duration of activities and the
arrival rate. Since the generated simulation model is supported by historical
data (event data)and it is based on the Discrete Event Simulation (DES)
technique, the generated event data is similar to the behavior of the real
process.
