Titel:
Multidimensional data modeling for business process analysis
Beteiligte:
Mansmann, Svetlana
[VerfasserIn];
Scholl, Marc H.
[VerfasserIn];
Neumuth, Thomas
[VerfasserIn]
Erschienen:
KOPS - The Institutional Repository of the University of Konstanz, 2007
Sprache:
Englisch
DOI:
https://doi.org/10.1007/978-3-540-75563-0_4
Entstehung:
Anmerkungen:
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Beschreibung:
The emerging area of business process intelligence attempts to enhance the analytical capabilities of business process management systems by employing data warehousing and mining technologies. This paper presents an approach to re-engineering the business process modeling in conformity with the multidimensional data model. Since the business process and the multidimensional model are driven by rather different objectives and assumptions, there is no straightforward solution to converging these models. Our case study is concerned with Surgical Process Modeling which is a new and promising subdomain of business process modeling. We formulate the requirements of an adequate multidimensional presentation of process data, introduce the necessary model extensions and propose the structure of the data cubes resulting from applying vertical decomposition into flow objects, such as events and activities, and from the dimensional decomposition according to the factual perspectives, such as function, organization, and operation. The feasibility of the presented approach is exemplified by demonstrating how the resulting multidimensional views of surgical workflows enable various perspectives on the data and build a basis for supporting a wide range of analytical queries of virtually arbitrary complexity. ; published