Suche nach „[M.] [Helfert]“ hat 1 Publikationen gefunden
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    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    Q. Yang, Mouzhi Ge, M. Helfert

    Developing Reliable Taxonomic Features for Data Warehouse Architectures

    2020 IEEE 22nd Conference on Business Informatics (CBI)


    ISBN: 978-1-7281-9926-9

    DOI: 10.1109/CBI49978.2020.00033

    Abstract anzeigen

    Since there is a large variety of data warehouse architectures with different structures and components, it is very difficult and time-consuming to systematically analyse them and obtain insights from those architectures. One effective way to understand those architectures is using a taxonomy to classify them. However, most of the taxonomic features are derived in an ad-hoc way and the reliability of those features is unknown. This paper therefore is to develop a set of reliable features by modeling different data warehouse architectures and further generate the structural knowledge represented by a taxonomy. This taxonomy is further validated by evaluating two real-world data warehouse architectures from IBM and Facebook.