Conference Proceedings

Filtering Spurious Events from Event Streams of Business Processes

SJ van Zelst, MF Sani, A Ostovar, R Conforti, M La Rosa, J Krogstie, HA Reijers

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Springer | Published : 2018


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 un- fold. 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 behaviour. Hence, applying these techniques on real data leads to results of inferior quality. In this paper, we propose a..

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Awarded by Australian Research Council

Funding Acknowledgements

This research is funded by the Australian Research Council (grant DP150103356), and the DELIBIDA research program supported by NWO.