Abstract
Predictive business process monitoring is concerned with continuously analyzing the events produced by the execution of a business process in order to predict as early as possible the outcome of each ongoing case thereof. Previous work has approached the problem of predictive process monitoring when the observed events carry structured data payloads consisting of attribute-value pairs. In practice, structured data often comes in conjunction with unstructured (textual) data such as emails or comments. This paper presents a predictive process monitoring framework that combines text mining with sequence classification techniques so as to handle both structured and unstructured event payloads. The framework has been evaluated with respect to accuracy, prediction earliness and efficiency on two real-life datasets.
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Notes
- 1.
Scripts available at https://github.com/irhete/PredictiveMonitoringWithText.
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This research is funded by the EU FP7 Programme (project SO-PC-Pro) and by the Estonian Research Council and by ERDF via the Software Technology and Applications Competence Centre (STACC).
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Teinemaa, I., Dumas, M., Maggi, F.M., Di Francescomarino, C. (2016). Predictive Business Process Monitoring with Structured and Unstructured Data. In: La Rosa, M., Loos, P., Pastor, O. (eds) Business Process Management. BPM 2016. Lecture Notes in Computer Science(), vol 9850. Springer, Cham. https://doi.org/10.1007/978-3-319-45348-4_23
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DOI: https://doi.org/10.1007/978-3-319-45348-4_23
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