LAUSR

Conformance checking detects deviations in business process executions. An online detection method is needed to give immediate response to anticipate possible impacts. The state-of-the-art online conformance checking is the Prefix-Alignment (PA) technique. However, this technique has a limitation of maintaining all of the administration data of cases in memory. In an online environment, the last event of a case is never known, whereas a PA requires last event information to release the case from memory to free up space for other cases. Hence, the PA does not meet the requirements of online conformance checking in processing infinite data of event stream without memory constraints. PA also has a complex state space search computation especially for large and complex process model references. In this paper, a Graph-Based Online Token Replay (GO-TR) method is proposed. This method takes benefit from Graph Database to adapts the Token-Based Replay (TBR) technique which has simple replay computation. We propose a Replay Image (RI) to store the case administration and develop a cypher based algorithm to simulate token replay on the RI to handle the event stream. We also propose a cypher-based algorithm to identify and replay invisible paths. The experiment results show that GO-TR has been successful in adapting TBR and solving the problem of wrong-placed tokens in TBR. GO-TR outperforms PA in yielding replay throughputs of relatively small amount of data in online conformance checking. In terms of memory usage, GO-TR shows its superiority over PA because it does not have memory limitations problems.