Filtering spurious events from event streams of business processes

van Zelst, Sebastiaan J., Fani Sani, Mohammadreza, Ostovar, Alireza, Conforti, Raffaele & La Rosa, Marcello (2018). Filtering spurious events from event streams of business processes. Advanced Information Systems Engineering – 30th International Conference, CAiSE 2018, Proceedings (pp. 35-52). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 10816 LNCS). Springer.


Process mining aims at gaining insights into business processes by analysing event data recorded during process execution. The 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, whereby techniques are applied on live event streams, as process executions unfold. Analysing event streams allows us to gain instant insights into business processes. However, current techniques assume the input stream to be completely free of noise and other anomalous behaviours. Hence, applying these techniques to real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to filter out spurious events from a live event stream. Our experiments show that we are able to effectively filter out spurious events from the input stream and, as such, enhance online process mining results.

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