Process mining with token carried data

Abstract

Process mining is to discover, monitor and improve real processes by extracting the knowledge from logs which are available in today's information systems. The existing process mining algorithms are based on the event logs where only the executions of tasks are recorded. In order to reduce the pre-processing efforts and strengthen the mining ability of the existing process mining algorithms, we have proposed a novel perspective to employ the data carried by tokens recorded in token log which tracks the changes of process resources for process mining in this study. The feasibility of the token logs is proved and the results of pairwise t-tests show that there is no big difference between the efforts that are taken by the same workflow system to generate the token log and the event log. Besides, a process mining algorithm (τ) based on the new log is proposed in this paper. With algorithm τ, the mining efficiency as well as the mining capability is improved compared to the traditional event-log-based mining algorithms. We have also developed three plug-ins on top of the existing workflow engine, process modeling and mining platforms (YAWL, PIPE and ProM) for proving the feasibility of token log and realizing the token log generation and algorithm τ.