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Mining conditional partial order graphs from event logs

Author
Mokhov, A.; Carmona, J.; Beaumont, J.
Type of activity
Journal article
Journal
Lecture notes in computer science
Date of publication
2016
Volume
9930
First page
114
Last page
136
DOI
https://doi.org/10.1007/978-3-662-53401-4_6 Open in new window
Repository
http://hdl.handle.net/2117/91088 Open in new window
URL
http://link.springer.com/chapter/10.1007%2F978-3-662-53401-4_6 Open in new window
Abstract
Process mining techniques rely on event logs: the extraction of a process model (discovery) takes an event log as the input, the adequacy of a process model (conformance) is checked against an event log, and the enhancement of a process model is performed by using available data in the log. Several notations and formalisms for event log representation have been proposed in the recent years to enable efficient algorithms for the aforementioned process mining problems. In this paper we show how Co...
Citation
Mokhov, A., Carmona, J., Beaumont, J. Mining conditional partial order graphs from event logs. "Lecture notes in computer science", 2016, vol. 9930, p. 114-136.
Keywords
Compact representation, Control flows, Current limitation, Event logs, Partial order, Process Modeling, Process mining, Representation of events
Group of research
ALBCOM - Algorithms, Computational Biology, Complexity and Formal Methods

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