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Figure from:Automated ETL Testing on the Data Quality of a Data Warehouse

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The proposed tool has been tested by carried out two different databases, including respectively 1) real data that loaded in a

Figure 4 The proposed tool has been tested by carried out two different databases, including respectively 1) real data that loaded in a

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International J ournal of Computer Applications (0975 — 8887) Volume 131 —No.16, December2015
International J ournal of Computer Applications (0975 — 8887) Volume 131 —No.16, December2015
Fig 2: The proposed Automating DQ Testing Framework
Fig 2: The proposed Automating DQ Testing Framework
Table 1: Quality Dimensions Definition.
Table 1: Quality Dimensions Definition.
Fig 4: ERD for source database. International J ournal of Computer Applications (0975 — 8887) Volume 131 —No.16, December2015 The data flow diagram for the ETL process is shown that the specific columns will be extracted from the source database and the extracted data will be compare with those in the DW table, only the new records will be added and the others will be rejected. Figure (5) displays the data flow for employee dimension table.
Fig 4: ERD for source database. International J ournal of Computer Applications (0975 — 8887) Volume 131 —No.16, December2015 The data flow diagram for the ETL process is shown that the specific columns will be extracted from the source database and the extracted data will be compare with those in the DW table, only the new records will be added and the others will be rejected. Figure (5) displays the data flow for employee dimension table.
Table 2: The Generated Test Cases Results.
Table 2: The Generated Test Cases Results.
After getting the TCs results from the previous table an evaluation of the system effectiveness is made by compare the detected defeats with the number of seeded defects .Figure (6) shows the seeded errors' types and there frequency in the tested data set.
After getting the TCs results from the previous table an evaluation of the system effectiveness is made by compare the detected defeats with the number of seeded defects .Figure (6) shows the seeded errors' types and there frequency in the tested data set.