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Process Mining as First-Order Classification Learning on Logs with Negative Events

Book Contribution - Book Chapter Conference Contribution

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. © 2008 Springer-Verlag Berlin Heidelberg.
Book: Lecture Notes in Computer Science
Pages: 42 - 53
ISBN:3540782370
Publication year:2008
BOF-keylabel:yes
IOF-keylabel:yes
Authors from:Higher Education
Accessibility:Closed