Abstract. Although there has been a lot of progress in developing pro-cess mining algorithms in recent years, no effort has been put in devel-oping a common means of assessing the quality of the models discovered by these algorithms. In this paper, we outline elements of an evaluation framework that is intended to enable (a) process mining researchers to compare the performance of their algorithms, and (b) end users to evalu-ate the validity of their process mining results. Furthermore, we describe two possible approaches to evaluate a discovered model (i) using existing comparison metrics that have been developed by the process mining re-search community, and (ii) based on the so-called k-fold-cross validation known from the machine learning community. To illustrate the applica-tion of these two approaches, we compared a set of models discovered by different algorithms based on a simple example log. 1
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