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Specification-driven predictive business process monitoring
University of Innsbruck, AUT.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0003-3818-4442
2019 (English)In: Software and Systems Modeling, ISSN 1619-1366, E-ISSN 1619-1374Article in journal (Refereed) Epub ahead of print
Abstract [en]

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. © 2019, The Author(s).

Place, publisher, year, edition, pages
Springer Verlag , 2019.
Keywords [en]
Automatic prediction model creation, Machine learning-based prediction, Prediction task specification language, Predictive business process monitoring, Process control, Process monitoring, Specification languages, Specifications, Automatic prediction, Business domain, Business Process, Business process monitoring, Historical process, Life events, Prediction model, Prediction tasks, Forecasting
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-18939DOI: 10.1007/s10270-019-00761-wISI: 000493704500001Scopus ID: 2-s2.0-85074869416OAI: oai:DiVA.org:bth-18939DiVA, id: diva2:1371896
Note

open access

Available from: 2019-11-21 Created: 2019-11-21 Last updated: 2019-12-18Bibliographically approved

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