# A Study on Data Profiling: Focusing on Attribute Value Quality Index
## Materials and Methods
### Research Model
#### 2.5.3 Data Value Quality Index Calculation Model  
Outlier refers to a value apart from other observation values [26; 29], and, in the case of \(\pm 3\) standard deviations, it is regarded as an extreme value [30]. This study used it to diagnose the data quality of numerical, date, and categorical data types. A flowchart for extracting attributes of the data value quality diagnosis target is shown in Figure 3.  
The attribute quality index calculation standard is shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Type** & \begin{tabular}{c} **Measurement** \\ **Item (k)** \\ \end{tabular} & \
\begin{tabular}{c} **Attribute Quality Index Applying** \\ **Criteria (\(\beta\))** \\ \end{tabular} & **Weight (\(\alpha\))** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Numeric, \\ date, \\ categorical date \\ \end{tabular} } & \multirow{3}{*}{Missing value} & Missing value = 0 & 0.0 \\  & & 0 \textless{} Missing value \(\leq\) 5\% & 1.2 \\ \cline{3-4}  & & 5\% \textless{} Number of missing values \  
The calculation formula of the attribute value quality index (AVQI) of the \(i\)-th attribute in the data table is expressed by Equation (7).  
\[AVQI_{i}=\left|1-\frac{\sum_{k=1}^{n}X_{k}}{\sum_{k=1}^{n}Y_{k}}\right|, \tag{7}\]  
where \(i\) is \(i\)-th attribute of the data table, \(k\) is a measurement item, \(n\) is the number of measurement items, \(X_{k}\) is the value multiplied by weight (\(\alpha\)) corresponding to the standard for applying the attribute quality index of the \(k\)-th measurement item, and \(Y_{k}\) is the number of records whose weight (\(\alpha\)) of measurement item (\(k\)) is more than 0.0 (but, the AVQI of the attribute is 0.0, in the case of not having the number of records falling on the standard for applying an attribute quality index other than whose weight is 0.0).  
\[X_{k}=\ \sum_{\beta=1}^{n}(\ \textit{number of data extracted by $\beta$ criteria }\times\alpha), \tag{8}\]  
where \(\beta\) is the attribute quality index applying standard falling on the measurement item, and \(\alpha\) is the weight of the attribute quality index application criteria.  
\[Y_{k}=\ \sum_{\beta=1}^{n}(\ \textit{number of data extracted $\beta$ criteria }), \tag{9}\]  
where \(\beta\) standard record number is the record number in which the weight (\(\alpha\)) of the attribute quality index applying standard (\(\beta\)) is other than 0.0. The attribute value quality index (AVQI) becomes lower in error as it approaches 0. The calculation formula for the structured data value quality index (SDVI) is shown in Equation (10).  
\[SDVQI=\ \left|1-\frac{\sum_{i=1}^{n}B_{i}}{\sum_{i=1}^{n}A_{i}}\right|, \tag{10}\]  
where \(i\) is the \(i\)-th attribute of the value quality index calculation target, \(n\) is the number of attributes for calculating the value quality index, \(B_{i}\) is the value multiplied by weight (\(\alpha\)) falling on the attribute quality index applying standard (\(\beta\)) of the \(i\)-th attribute, and \(A_{i}\) is the number of records falling on the attribute quality index applying standard (\(\beta\)) of the \(i\)-th attribute. The structured data value quality index (SDVQI) becomes lower in error as it approaches 0.