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It is common in practice, e. g., due to logging errors in information systems or the presence of exeptional process behavior, to have outlier behavior in real event data. Such behavior often leads to incomprehensible, complex, and inaccurate analysis results and makes correct and/or important behavior undetectable. In this paper, we propose a novel data preprocessing method, that detects and subsequently repairs outlier behavior in event data. We propose a probabilistic method that detectsdoi:10.18417/emisa.14.5 dblp:journals/emisaij/SaniZA18 fatcat:aj2yok6oijh6lkflhzpxxyuxde