Christian Lettner, Reinhard Stumptner, Werner Fragner, Franz Rauchenzauner, Lisa Ehrlinger,
"DaQL 2.0: Measure Data Quality based on Entity Models"
: Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020), Serie Procedia Computer Science, Vol. 180, Elsevier B.V., Seite(n) 772-777, 2021
Original Titel:
DaQL 2.0: Measure Data Quality based on Entity Models
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020)
Original Kurzfassung:
In order to make good decisions, the data used for decision-making needs to be of high quality. As the volume of data continually increases, ensuring high data quality is a big challenge nowadays and needs to be automated with tools. The goal of the Data Quality Library (DaQL) is to provide a tool to continuously ensure and measure data quality as proposed in [5]. In this paper, we present the current status of the development of the new DaQL version 2.0. The main contribution of DaQL 2.0 is the possibility to define data quality rules for complex data objects (called entities), which represent business objects. In contrast to existing tools, a user does not require detailed knowledge about the database schema that is observed.
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