Abstract
Process mining is a research discipline that aims to discover, monitor and improve real processing using event logs. In this paper we describe a novel approach that (i) identifies partial process models by exploiting sequential pattern mining and (ii) uses the additional information about the activities matching a partial process model to train nested prediction models from event logs. Models can be used to predict the next activity and completion time of a new (running) process instance. We compare our approach with a model based on Transition Systems implemented in the ProM5 Suite and show that the attributes in the event log can improve the accuracy of the model without decreasing performances. The experimental results show how our algorithm improves of a large margin ProM5 in predicting the completion time of a process, while it presents competitive results for next activity prediction.
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Ceci, M., Lanotte, P.F., Fumarola, F., Cavallo, D.P., Malerba, D. (2014). Completion Time and Next Activity Prediction of Processes Using Sequential Pattern Mining. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds) Discovery Science. DS 2014. Lecture Notes in Computer Science(), vol 8777. Springer, Cham. https://doi.org/10.1007/978-3-319-11812-3_5
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DOI: https://doi.org/10.1007/978-3-319-11812-3_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11811-6
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