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17.2.2   DAL and TAL validity checking             validation is required to make sure this indicator is
            In order to correctly combine the data from different   indeed unique, and data can be assigned without
            sources (e.g., T1 to T6 from the A-party side with T7   creating gaps or duplicates.
            from the B-party side), information about the pairing   It is also helpful to produce unique scenario identi-
            of teams, and temporal assignment to DFS testing   fiers as numbers. A scenario name will typically be
            scenarios,  is  required.  This  information  is  provid-  a rather long string of text which may be impracti-
            ed by the DAL (see also section 11.7) and TAL (see   cal to be used in dense tables or graphs. A scenario
            section 11.1). Therefore, DAL and TAL are also import-  index, in combination with  a look-up table, makes
            ed to the database, and after validity checking, typi-  labelling easier.
            cally processed into  respective internal tables  with   For TAL validation, it is helpful to create a visualiza-
            added (constructed) content.                       tion of the TAL in the form of a GANTT diagram as
            In order to process data, a unique scenario descrip-  shown in the example below. With the help of such
            tor is required which is used to aggregate (group)   a visualization, it can easily be checked if all scenar-
            data to respective KPI. Depending on the actual TAL   io/time ranges are present and consistent. Also, the
            structure, such a descriptor can either be included   source table for this visualization can be used to
            in the TAL directly, or – preferably – be constructed   check the scenario names for uniqueness.
            in the data base from basic elements. In any case,

            Figure 14 – Example of a GANTT visualization of a TAL



































            17�3 Data Processing                               The basis of these join operations is information
            A good practice for data processing is to import data   relating the data items to scenarios. This is done in a
            from MSW and network background testing into a     multi-step operation. In the first step, technical iden-
            central database, and run the final processing there.  tifiers are used to connect to the configurations or
            In the first step of processing, MSW and network   owner teams. This can be done by creating a look-up
            KPI are combined (joined) with respective scenario   table from the DAL with respective join operations,
            and team information. In the second step, respective   or by assigning these elements directly in respec-
            grouping of DFS and network KPI is done based on   tive SQL statements when the processed MSW and
            this information.                                  network KPI tables are created.





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