Journal article

Detection and removal of infrequent behavior from event streams of business processes

SJ van Zelst, M Fani Sani, A Ostovar, R Conforti, M La Rosa

Information Systems | Elsevier Ltd | Published : 2020


Process mining aims at gaining insights into business processes by analyzing the event data that is generated and recorded during process execution. The vast majority of existing process mining techniques works offline, i.e. using static, historical data, stored in event logs. Recently, the notion of online process mining has emerged, in which techniques are applied on live event streams, i.e. as the process executions unfold. Analyzing event streams allows us to gain instant insights into business processes. However, most online process mining techniques assume the input stream to be completely free of noise and other anomalous behavior. Hence, applying these techniques to real data leads t..

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Awarded by Australian Research Council

Funding Acknowledgements

This research is funded by the Australian Research Council (grant DP180102839)