Within the process mining domain, research oncomparing control-flow (CF) discovery techniques hasgained importance. A crucial building block of empiricalanalysis of CF discovery techniques is obtaining theappropriate evaluation data. Currently, there is no answerto the question of how to collect such evaluation data. Thepaper introduces a methodology for generating artificialevent data (GED) and an implementation called the ProcessTree and Log Generator. The GED methodology and itsimplementation provide users with full control over thecharacteristics of the generated event data and an integra-tion within the ProM framework. Unlike existing approa-ches, there is no tradeoff between including long-termdependencies and soundness of the process. The contribu-tions of the paper provide a solution for a necessary step inthe empirical analysis of CF discovery algorithms.
"Generating Artificial Data for Empirical Analysis of Control-flowDiscovery Algorithms, A Process Tree and Log Generator,"
Business & Information Systems Engineering:
Vol. 61: Iss. 6, 695-712.
Available at: https://aisel.aisnet.org/bise/vol61/iss6/5