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Process mining represents an important field in BPM and data mining research.Recently, it has gained importance also for practitioners: more and morecompanies are creating business process intelligence solutions. The evaluationof process mining algorithms requires, as any other data mining task, theavailability of large amount of real-world data. Despite the increasingavailability of such datasets, they are affected by many limitations, in primisthe absence of a "gold standard" (i.e., the reference model).  This paper extends an approach, already available in the literature, for thegeneration of random processes. Novelties have been introduced throughout thework and, in particular, they involve the complete support for multiperspectivemodels and logs (i.e., the control-flow perspective is enriched with time anddata information) and for online settings (i.e., generation of multiperspectiveevent streams and concept drifts). The proposed new framework is able to almostentirely cover the spectrum of possible scenarios that can be observed in thereal-world. The proposed approach is implemented as a publicly available Javaapplication, with a set of APIs for the programmatic execution of experiments.

PLG2: Multiperspective Processes Randomization and Simulation for Online and Offline Settings