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2020 | OriginalPaper | Buchkapitel

Extracting Multiple Viewpoint Models from Relational Databases

verfasst von : Alessandro Berti, Wil van der Aalst

Erschienen in: Data-Driven Process Discovery and Analysis

Verlag: Springer International Publishing

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Abstract

Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually applying techniques to discover, control and predict the business process. Moreover, current process mining techniques assume a single case notion. However, in real-life processes often different case notions are intertwined. For example, events of the same order handling process may refer to customers, orders, order lines, deliveries, and payments. Therefore, we propose to use Multiple Viewpoint (MVP) models that relate events through objects and that relate activities through classes. The required event data are much closer to existing relational databases. MVP models provide a holistic view on the process, but also allow for the extraction of classical event logs using different viewpoints. This way existing process mining techniques can be used for each viewpoint without the need for new data extractions and transformations. We provide a toolchain allowing for the discovery of MVP models (annotated with performance and frequency information) from relational databases. Moreover, we demonstrate that classical process mining techniques can be applied to any selected viewpoint.

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Titel: Extracting Multiple Viewpoint Models from Relational Databases
verfasst von: Alessandro Berti
Wil van der Aalst
Verlag: Springer International Publishing
Buch: Data-Driven Process Discovery and Analysis
Print ISBN: 978-3-030-46632-9

Electronic ISBN: 978-3-030-46633-6

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-46633-6_2

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