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Time prediction based on process mining
| Venue: | Information Systems |
| Citations: | 34 - 1 self |
BibTeX
@ARTICLE{Schonenberg_timeprediction,
author = {M. H. Schonenberg and M. Song},
title = {Time prediction based on process mining},
journal = {Information Systems},
year = {},
pages = {2011}
}
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Abstract
Abstract. Process mining allows for the automated discovery of pro-cess 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 pre-dict 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 pro-cessing 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 comple-tion 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. 1
Keyphrases
process mining time prediction comple-tion time insurance company configurable approach event log reliable time prediction good balance completion time enable various type meaningful time prediction discovered process model many scenario pro-cess model automated discovery real-life event model-based analysis pro-cessing time several experiment process model configurable set time information insurance claim
