Skip to Main content Skip to Navigation
Conference papers

Extracting Multiple Viewpoint Models from Relational Databases

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.
Document type :
Conference papers
Complete list of metadata

https://hal.inria.fr/hal-03188589
Contributor : Hal Ifip <>
Submitted on : Friday, April 2, 2021 - 3:42:04 PM
Last modification on : Friday, April 2, 2021 - 3:44:47 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2023-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Alessandro Berti, Wil Aalst. Extracting Multiple Viewpoint Models from Relational Databases. 8th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Dec 2018, Seville, Spain. pp.24-51, ⟨10.1007/978-3-030-46633-6_2⟩. ⟨hal-03188589⟩

Share

Metrics

Record views

11