Journal article
Filtering Out Infrequent Behavior from Business Process Event Logs
Raffaele Conforti, Marcello La Rosa, Arthur HM ter Hofstede
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | IEEE COMPUTER SOC | Published : 2017
Abstract
In the era of "big data", one of the key challenges is to analyze large amounts of data collected in meaningful and scalable ways. The field of process mining is concerned with the analysis of data that is of a particular nature, namely data that results from the execution of business processes. The analysis of such data can be negatively influenced by the presence of outliers, which reflect infrequent behavior or "noise". In process discovery, where the objective is to automatically extract a process model from the data, this may result in rarely travelled pathways that clutter the process model. This paper presents an automated technique to the removal of infrequent behavior from event log..
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Awarded by ARC
Awarded by Australian Centre for Health Services Innovation
Funding Acknowledgements
This research is funded by the ARC Discovery Project DP150103356, and supported by the Australian Centre for Health Services Innovation #SG00009-000450.