A workflow net similarity measure based on transition adjacency relations

Many activities in business process management, such as process retrieval, process mining, and process integration, need to determine the similarity or the distance between two processes. Although several approaches have recently been proposed to measure the similarity between business processes, neither the definitions of the similarity notion between processes nor the measure methods have gained wide recognition. In this paper, we define the similarity and the distance based on firing sequences in the context of workflow nets (WF-nets) as the unified reference concepts. However, to many WF-nets, either the number of full firing sequences or the length of a single firing sequence is infinite. Since transition adjacency relations (TARs) can be seen as the genes of the firing sequences which describe transition orders appearing in all possible firing sequences, we propose a practical similarity definition based on the TAR sets of two processes. It is formally shown that the corresponding distance measure between processes is a metric. An algorithm using model reduction techniques for the efficient computation of the measure is also presented. Experimental results involving comparison of different measures on artificial processes and evaluations on clustering real-life processes validate our approach.