Abstract
Process mining is the automated construction of process models from information system event logs. In this paper we identify three fundamental difficulties related to process mining: the lack of negative information, the presence of history-dependent behavior and the presence of noise. These difficulties can elegantly dealt with when process mining is represented as first-order classification learning on event logs supplemented with negative events. A first set of process discovery experiments indicates the feasibility of this learning technique.
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Goedertier, S., Martens, D., Baesens, B., Haesen, R., Vanthienen, J. (2008). Process Mining as First-Order Classification Learning on Logs with Negative Events. In: ter Hofstede, A., Benatallah, B., Paik, HY. (eds) Business Process Management Workshops. BPM 2007. Lecture Notes in Computer Science, vol 4928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78238-4_6
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DOI: https://doi.org/10.1007/978-3-540-78238-4_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78237-7
Online ISBN: 978-3-540-78238-4
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