Sani, Mohammadreza Fanivan Zelst, Sebastiaan Jvan der Aalst, Wil M. P2020-06-152020-06-152019https://dl.gi.de/handle/20.500.12116/33220It 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 detects outlier behavior based on the occurrence probability of a sequence of activities among its surronding contextual behavior. We replace the outlier behavior with more probable behavior among that behavioral context. Our approach allows to remove outlier behavior, which enables us to obtain a more global view of the process. The proposed method has been implemented in both the prom- and the rapidprom frameworks. Using these implementations, we conducted several experiments that show that most types of outlier behavior in event data are detectable and repairable via the proposed method. The evaluation clearly demonstrates that we are able to improve process discovery results by repairing event logs upfront. Results show that using the proposed method we obtain more understandable process models with higher accuracy.enProcess MiningData CleansingLog RepairEvent Log PreprocessingOutlier DetectionConditional ProbabilityText/Journal Article10.18417/emisa.14.51866-3621