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Song, Minseok
Business Process Intelligence(BPI) Lab
Research Interests
  • Process mining

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Time prediction based on process mining

Cited 31 times inthomson ciCited 54 times inthomson ci
Title
Time prediction based on process mining
Author
van der Aalst, W. M. P.Schonenberg, M. H.Song, Minseok
Keywords
Business intelligence; Business process management; Performance analysis; Process mining; Time prediction
Issue Date
201104
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
INFORMATION SYSTEMS, v.36, no.2, pp.450 - 475
Abstract
Process mining allows for the automated discovery of process models from event logs. These models provide insights and enable various types of model-based analysis. This paper demonstrates that the discovered process models can be extended with information to predict the completion time of running instances. There are many scenarios where it is useful to have reliable time predictions. For example, when a customer phones her insurance company for information about her insurance claim, she can be given an estimate for the remaining processing time. In order to do this, we provide a configurable approach to construct a process model, augment this model with time information learned from earlier instances, and use this to predict e.g., the completion time. To provide meaningful time predictions we use a configurable set of abstractions that allow for a good balance between "overfitting" and "underfitting". The approach has been implemented in ProM and through several experiments using real-life event logs we demonstrate its applicability.
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DOI
http://dx.doi.org/10.1016/j.is.2010.09.001
ISSN
0306-4379
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