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Process mining methods allow analysts to use logs of historical executions ofbusiness processes in order to gain knowledge about the actual behavior ofthese processes. One of the most widely studied process mining operations isautomated process discovery. An event log is taken as input by an automatedprocess discovery method and produces a business process model as output thatcaptures the control-flow relations between tasks that are described by theevent log. In this setting, this paper provides a systematic comparativeevaluation of existing implementations of automated process discovery methodswith domain experts by using a real-life event log extracted from aninternational software engineering company and four quality metrics. Theevaluation results highlight gaps and unexplored trade-offs in the field andallow researchers to improve the lacks in the automated process discoverymethods in terms of usability of process discovery techniques in industry.

A User Evaluation of Automated Process Discovery Algorithms