Specification-driven predictive business process monitoring
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SPECIAL SECTION PAPER
Specification-driven predictive business process monitoring Ario Santoso1,2
· Michael Felderer1,3
Received: 30 November 2018 / Accepted: 5 October 2019 © The Author(s) 2019
Abstract Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs. Keywords Predictive business process monitoring · Prediction task specification language · Automatic prediction model creation · Machine learning-based prediction
1 Introduction Process mining [66,67] provides a collection of techniques for extracting process-related information from the logs of business process executions (event logs). One important area in this field is predictive business process monitoring, which aims at forecasting the future information of a running process based on the models extracted from event logs. Through Communicated by Dr. Rainer Schmidt and Jens Gulden. This research has been supported by the Euregio Interregional Project Network IPN12 “Knowledge-Aware Operational Support” (KAOS), which is funded by the “European Region Tyrol-South Tyrol-Trentino” (EGTC) under the first call for basic research projects.
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Ario Santoso [email protected]; [email protected] Michael Felderer [email protected]
1
Department of Computer Science, University of Innsbruck, Innsbruck, Austria
2
Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy
3
Department of Software Engineering, Blekinge Institute of Technology, Karlskrona, Sweden
predictive analysis, potential future problems can be detected and preventive actions can be taken in order to avoid unexpected situation, e.g., processing delay and service-level agreement (SLA) violations. Many studies have been conducted in order to deal with various prediction tasks such as predicting the remaining processing time [52–54,63,69], predicting the outcomes of a process [18,35,50,72], and predicting future events [19,24,63] (cf.
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