Finally, the “Verify Data Cribility” activity will be responsible for managing and adding the DQ metadata (stor in an instance of the “DQ_Metadata” class), with the aim of guaranteeing the DQ requirement of “Confirm Data Cribility”, which will be relat to the respective Billing data (of type “Content”).
Conclusions and future work
Correct management of data quality requirements significantly contributes to eliminating or at least minimizing potential data problems, allowing information system users to carry out their bolivia phone number library operations with a higher level of confidence.
This article presents a propos solution
Bas on the model-driven development approach, presenting an extend metamodel (DQ_WebRE) for managing data quality requirements bas on the metamodel (WebRE). By specifying the boundary of perception this metamodel and using the develop plugin, it is possible to manage and model key aspects of DQ from the initial stage of the development process, which will allow developers to be aware of DQ requirements.
As future work, the incorporation of model transformation australia aatabase airectory mechanisms or rules is plann, using the QVT This will allow obtaining different design models from analysis models and subsequently the semi-automatic generation of code.
Figure 3. Use case diagram specifying DQ requirements.