DISCOVERING BLOCK-STRUCTURED PARALLEL PROCESS MODELS FROM CAUSALLY COMPLETE EVENT LOGS
Julijana LekiŠ – Dragan MiliŠev
α-algorithm is suitable to discover a large class of workflow (WF) nets based on the behaviour recorded in event logs, with the main limiting assumption that the event log is complete. Our research has been aimed at finding ways of discovering business process models based on examples of traces, ie, logs of workflow actions that do not meet the requirement of completeness. In this aim, we have modified the existing and introduced a new relation between activities recorded in the event log, which has led to a partial correction of the process models discovering technique, including the α-algorithm. We have also introduced the notion of causally complete logs, from which our modified algorithm can produce the same result as the α-algorithm from complete logs. The effect of these modifications on the efficiency of the process model discovering is mostly evident for business processes in which many activities can be performed in parallel. The application of the modified method for discovering block-structured models of parallel business processes is presented in this paper.
Keywords: process mining, business process model discovery, block-structured parallel process models, complete log, α-algorithm