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, Name of problem file: Subject to the domain chosen, this argument inputs the domain-specific problem file specific to SRMLearn. This problem file is used to isolate the variables in the traces and their corresponding types. This will then be used in the annotation and generalization phase to replace the variables in the traces with their corresponding types. Name of domain file: Subject to the domain chosen, this argument inputs the domain file to SRMLearn. This domain file serves to compare the difference between the learnt empirical model and the ground truth action model, and thus calculate the accuracy of SRMLearn, Number of tested traces: This argument selects the number of traces out of a maximum of 1000 that will be used as input to SRMLearn. It is a positive integer between 1 and 1000

. Fournier-viger, Choice of data mining algorithm: This parameter inputs the chosen data mining algorithm in order to find the frequent action pairs and use them as short term constraints. The available algorithms include the apriori algorithm 3 or the TRuleGrowth algorithm 5. These form part of the SPMF mining library, 2014.

. Confidence, this parameter allows to set the minimum confidence that the mined action pairs must satisfy. This parameter is used by the SRMLearn system as an input parameter to the SPMF API during an internal call to this API during SRMLearn's course of execution, case the mining algorithm chosen happens to be "trulegrowth

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