Table 3 Mapping of assessment methods and context independent DQ problems
Related Figures (3)
There are many types of DQ problems in organizations and several researchers have taken a closer look at DQ problems and root causes [17],[7],[13],[21]. DQ problems can be classified into problems that exist independently of a specific context, e.g. spelling errors and duplicate data, and problems that depend on the context of use, e.g. violation of company and government regulations [9]. Furthermore, these problems can be seen from a user perspective, which are the problems recognized by an information consumer, or a data perspective, which are often the root causes of the problems that information consumers have to face. As the paper is aiming at classifying DQ assessment methods in the context of relational databases, this The existing taxonomy of DQ problems consists of elements at various levels of granularity [17]. These evels relate to the well-known relational database structure which includes: attributes (fields or columns), rows (records or tuples), tables (or relations) and the database (multiple tables). Furthermore, the taxonomy also includes a level that relates to multiple databases. The different elements of the taxonomy are shown in Table 2, which includes the taxonomy element (an acronym of the element), the name of the element, and a mapping requirement. The mapping requirement (not described in the taxonomy) is usec for this work to aid the mapping of the DQ methods into an element of the taxonomy for a particular DQ problem. This mapping requirement specifies what the DQ method must meet in order to find a particula DQ problem. For example, the domain analysis method only needs to consider whether the value of one attribute lies in the domain (which is external information) in order to determine whether there is an incorrect value, thus, it is classified as SAST (see the first row of Table 2) for the incorrect value DQ problem. Note that the domain checking method can be also applied to all rows in a table (by applying the method multiple times), but the algorithm requires only a single row and single attribute to judge if there is a DQ Table 4. Mapping of assessment methods and context dependent DQ problems Table 4 presents the results of the classification for the context- dependent data perspective DQ problem with the taxonomy elements across the top and the DQ problems shown vertically. However, in contra: to the context-independent classification, no gaps were identified this time. Each item in the table | discussed in the next section in further detail.
![There are many types of DQ problems in organizations and several researchers have taken a closer look at DQ problems and root causes [17],[7],[13],[21]. DQ problems can be classified into problems that exist independently of a specific context, e.g. spelling errors and duplicate data, and problems that depend on the context of use, e.g. violation of company and government regulations [9]. Furthermore, these problems can be seen from a user perspective, which are the problems recognized by an information consumer, or a data perspective, which are often the root causes of the problems that information consumers have to face. As the paper is aiming at classifying DQ assessment methods in the context of relational databases, this](https://figures.academia-assets.com/50830569/table_001.jpg)
![The existing taxonomy of DQ problems consists of elements at various levels of granularity [17]. These evels relate to the well-known relational database structure which includes: attributes (fields or columns), rows (records or tuples), tables (or relations) and the database (multiple tables). Furthermore, the taxonomy also includes a level that relates to multiple databases. The different elements of the taxonomy are shown in Table 2, which includes the taxonomy element (an acronym of the element), the name of the element, and a mapping requirement. The mapping requirement (not described in the taxonomy) is usec for this work to aid the mapping of the DQ methods into an element of the taxonomy for a particular DQ problem. This mapping requirement specifies what the DQ method must meet in order to find a particula DQ problem. For example, the domain analysis method only needs to consider whether the value of one attribute lies in the domain (which is external information) in order to determine whether there is an incorrect value, thus, it is classified as SAST (see the first row of Table 2) for the incorrect value DQ problem. Note that the domain checking method can be also applied to all rows in a table (by applying the method multiple times), but the algorithm requires only a single row and single attribute to judge if there is a DQ](https://figures.academia-assets.com/50830569/table_002.jpg)
