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 work extends an approach already available in the literature for the generation of random processes. Novelties have been introduced throughout the work which, in particular, 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 cover the spectrum of possible scenarios that can be observed in the real-world.
Published in Online Proceedings of the BPM Demo Track 2016; Rio de Janeiro, Brasil; September, 18 2016; CEUR-WS.org 2016.