Journal
INFORMATION SYSTEMS
Volume 36, Issue 2, Pages 450-475Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.is.2010.09.001
Keywords
Process mining; Business process management; Time prediction; Performance analysis; Business intelligence
Categories
Funding
- EIT
- NWO-EW
- SUPER
- Technology Foundation STW
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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. (C) 2010 Elsevier B.V. All rights reserved.
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