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An XES Extension for Uncertain Event Data
[article]
2022
arXiv
pre-print
Event data, often stored in the form of event logs, serve as the starting point for process mining and other evidence-based process improvements. However, event data in logs are often tainted by noise, errors, and missing data. Recently, a novel body of research has emerged, with the aim to address and analyze a class of anomalies known as uncertainty-imprecisions quantified with meta-information in the event log. This paper illustrates an extension of the XES data standard capable of
arXiv:2204.04135v1
fatcat:yrhcb426rfcyfpmss4m64sm2ca