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. Thus, different from previous studies, our approach enables us to deal with various kinds of prediction tasks based on the given specification. A prototype implementing our approach has been developed and experiments using a real-life event log have been conducted.
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Notes
- 1.
We assume that variables are standardized apart, i.e., no two quantifiers bind the same variable (e.g., \(\forall i . \exists i . (i > 3)\)), and no variable occurs both free and bound (e.g., \((i> 5) ~\wedge ~\exists i . (i > 3)\)). As usual in FOL, every FOE formula can be transformed into a semantically equivalent formula where the variables are standardized apart by applying some variable renaming [28].
- 2.
Note that timestamp can be represented as milliseconds since epoch (hence, it is a number).
- 3.
More information about the implementation architecture, the code, the tool, and the screencast can be found at http://bit.ly/predictive-analysis.
- 4.
ProM is an extendable framework for process mining (http://www.promtools.org).
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Acknowledgement
This research has been supported by the Euregio IPN12 “KAOS: Knowledge-Aware Operational Support” project, which is funded by the “European Region Tyrol-South Tyrol-Trentino” (EGTC) under the first call for basic research projects. The author thanks Tri Kurniawan Wijaya for various suggestions related to this work, and Yasmin Khairina for the implementation of some prototype components.
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Santoso, A. (2018). Specification-Driven Multi-perspective Predictive Business Process Monitoring. In: Gulden, J., Reinhartz-Berger, I., Schmidt, R., Guerreiro, S., Guédria, W., Bera, P. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2018 2018. Lecture Notes in Business Information Processing, vol 318. Springer, Cham. https://doi.org/10.1007/978-3-319-91704-7_7
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