Abstract
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.
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This work was supported by the European Social Fund via "ICT programme" measure, the European Regional Development Fund, and the programme Mobilitas Pluss (2014-2020.4.01.16-0024).
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Raun, K., Nielsen, M., Burattin, A., Awad, A. (2023). C-3PA: Streaming Conformance, Confidence and Completeness in Prefix-Alignments. In: Indulska, M., Reinhartz-Berger, I., Cetina, C., Pastor, O. (eds) Advanced Information Systems Engineering. CAiSE 2023. Lecture Notes in Computer Science, vol 13901. Springer, Cham. https://doi.org/10.1007/978-3-031-34560-9_26
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