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• W4313155918 abstract "In this article we introduce <inline-formula><tex-math notation=LaTeX>${{sf STARDUST}}$</tex-math></inline-formula> (event STream Analysis for pRocess Discovery Using Sampling sTragies), a process discovery approach that analyses a trace stream, in order to discover a process model that may change over time. The basic idea is to adopt a sampling technique to select the most representative trace variants to be considered for the process discovery, then to alert a concept drift as the trace variants to be sampled change over time and, finally, to trigger the discovery of a new process model as a drift is alerted. We formulate the proposed approach under the assumption that the trace distribution commonly follows the Pareto's principle (i.e., a few trace variants covers the majority of cases) which is commonly satisfied in several business processes. Experimental results on various benchmark event logs handled as streams show the effectiveness of the proposed approach also compared to a state-of-the- art concept drift detection approach." @default.
• W4313155918 created "2023-01-06" @default.
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• W4313155918 date "2023-07-01" @default.
• W4313155918 modified "2023-09-29" @default.
• W4313155918 title "STARDUST: A Novel Process Mining Approach to Discover Evolving Models From Trace Streams" @default.
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• W4313155918 doi "https://doi.org/10.1109/tsc.2022.3215502" @default.
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