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