Technical University of Denmark
The aim of streaming conformance checking is to find discrepancies between process executions on streaming data and the reference process model. The state-of-the-art output from streaming conformance checking is a prefix-alignment. However, current techniques that output a prefix-alignment are unable to handle warm-starting scenarios. Further, no indication is given of how close the trace is to termination - a highly relevant measure in a streaming setting.
This paper introduces a novel approximate streaming conformance checking algorithm that enriches prefix-alignments with confidence and completeness measures. Empirical tests on synthetic and real-life datasets demonstrate that the new method outputs prefix-alignments that have a cost that is highly correlated with the output from the state-of-the-art optimal prefix-alignments. Furthermore, the method is able to handle warm-starting scenarios and indicate the confidence level of the prefix-alignment. A stress test shows that the method is well-suited for fast-paced event streams.
In Proceedings of CAiSE 2023; Zaragoza, Spain; June 2023.
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