Abstract
By means of deriving knowledge from event logs, the application of process mining algorithms can provide valuable insight into the actual execution of business processes and help identify opportunities for their improvement. The event logs may be collected by people manually or generated by a variety of software applications, including business process management systems. However logging may not always be done in a reliable manner, resulting in events being missed or interchanged. Consequently, the results of the application of process mining algorithms to such “polluted” logs may not be so reliable and it would be preferable if false traces, i.e. polluted traces which are not possibly valid as regards the process model to be discovered, could be identified first and removed before such algorithms are applied. In this paper an approach is proposed that assists with identifying false traces in event logs as well as the cause of their pollution. The approach is empirically validated.
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Yang, H., Wen, L., Wang, J. (2013). An Approach to Identifying False Traces in Process Event Logs. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_45
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DOI: https://doi.org/10.1007/978-3-642-37456-2_45
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