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2016 | OriginalPaper | Buchkapitel

Predictive Business Process Monitoring with Structured and Unstructured Data

verfasst von : Irene Teinemaa, Marlon Dumas, Fabrizio Maria Maggi, Chiara Di Francescomarino

Erschienen in: Business Process Management

Verlag: Springer International Publishing

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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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Vorheriges Kapitel PRISM – A Predictive Risk Monitoring Approach for Business Processes

Nächstes Kapitel P-Folder: Optimal Model Simplification for Improving Accuracy in Process Performance Prediction

Scripts available at https://github.com/irhete/PredictiveMonitoringWithText.

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Titel: Predictive Business Process Monitoring with Structured and Unstructured Data
verfasst von: Irene Teinemaa
Marlon Dumas
Fabrizio Maria Maggi
Chiara Di Francescomarino
Verlag: Springer International Publishing
Buch: Business Process Management
Print ISBN: 978-3-319-45347-7

Electronic ISBN: 978-3-319-45348-4

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-45348-4_23

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