Abstract
Volatile environments force companies to adapt their processes, leading to so called concept drifts during run-time. Concept drifts do not only affect the control flow, but also process data. An example are manufacturing processes where a multitude of machining parameters are necessary to drive the production and might be subject to change due to e.g., machine errors. Detecting such data drifts immediately can help to trigger exception handling in time and to avoid gradual deterioration of the process execution quality. This paper provides online algorithms for concept drift detection in process data employing the concept of process histories. The feasibility of the algorithms is shown based on a prototypical implementation and the analysis of a real-world data set from the manufacturing domain.
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Acknowledgment
This work has been partly funded by the Vienna Science and Technology Fund (WWTF) through project ICT15-072 and by the Austrian Research Promotion Agency (FFG) via the “Austrian Competence Center for Digital Production” (CDP) under the contract number 854187.
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Stertz, F., Rinderle-Ma, S. (2019). Detecting and Identifying Data Drifts in Process Event Streams Based on Process Histories. In: Cappiello, C., Ruiz, M. (eds) Information Systems Engineering in Responsible Information Systems. CAiSE 2019. Lecture Notes in Business Information Processing, vol 350. Springer, Cham. https://doi.org/10.1007/978-3-030-21297-1_21
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DOI: https://doi.org/10.1007/978-3-030-21297-1_21
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