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• W3036767319 abstract "Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). This may lead to a proliferation of differences due to the low level of granularity in which such differences are captured. This paper presents a novel approach to detect statistically-significant differences between variants at the level of entire process traces (i.e. sequences of directly-follows relations). The cornerstone of this approach is a technique to learn a directly-follows graph called mutual fingerprint from the event logs of the two variants. A mutual fingerprint is a lossless encoding of a set of traces and their duration using discrete wavelet transformation. This structure facilitates the understanding of statistical differences along the control-flow and performance dimensions. The approach has been evaluated using real-life event logs against two baselines. The results show that at a trace level, the baselines cannot always reveal the differences discovered by our approach, or can detect spurious differences." @default.
• W3036767319 created "2020-06-25" @default.
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• W3036767319 date "2020-01-01" @default.
• W3036767319 modified "2023-10-10" @default.
• W3036767319 title "Business Process Variant Analysis Based on Mutual Fingerprints of Event Logs" @default.
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• W3036767319 doi "https://doi.org/10.1007/978-3-030-49435-3_19" @default.
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