# Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application
## 6 Result and Discussion  
To evaluate the proposed data quality assessment model, an ample number of experiments have been conducted and repeated in order to reduce the chance of errors. In the first experiment, we analyzed the ASC and TMC applications CVOs' contextual data quality assessment with respect to the completeness metric parameters. In the second experiment, we perform the data quality assessment with respect to the appropriate amount of data quality metrics parameters and in the third experiment, the overall data quality assessment of ASC and TMC VOs and CVOs semantic data has been performed. We also analyzed the data acquisition cost for the semantic data of VOs and CVOs. These data acquisition functions have been implemented with containerized microservices that have many numbers of instances to process semantic VOs and CVOs data from the repositories. The VOs and CVOs semantic data repository has been created using the apache Jena triple store [61]. In all the experiments of data quality assessment learning model, the datasets have been distributed into 80% and 20%. The 80% data is used in the model training and tuning the parameters; and the samples from 20% data is utilized in the model testing and evaluation, because to keep the testing and evaluation dataset samples away from the training and tuning samples is an independent evaluation approach [62; 63]. The deep learning model has been configured to 100 epochs with an early stopping parameter setting as used in [64] and in this setting the developed model converged quite well. We evaluate our data quality assessment deep learning models efficiency based on the f-measure, mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation coefficient (PCC) metrics. The deep data \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multirow{2}{*}{**Manipulation**} & \multicolumn{2}{c}{**Random Sample Size of**} & \multirow{2}{*}{**Description**} \\ \cline{3-3} \cline{5-5}  & & & **Manipulated Data** & & \\ \cline{3-3} \cline{5-5}  & & **Dataset A** & & **Dataset B** & \\ \hline \hline \multirow{4}{*}{1} & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multicolumn{3}{c}{The original datasets A and B samples} \\  & & & & have been used without any & \\ \cline{3-3} \cline{5-5}  & & & & manipulation with the ASC and TMC & applications VOs and CVOs ontology. & \\ \cline{3-3} \cline{5-5}  & & & & It is assumed that VOs and CVOs data & quality with respect to contextual metric \\ \cline{3-3} \cline{5-5}  & & & & parameters is 100\%. & \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{10} & \multirow{4}{*}{12,793.2} & \multirow{4}{*}{5,955,768} & For the second test case, the 10\% data has & \\  & & & & been randomly selected for the & \\ \cline{3-3} \cline{5-5}  & & & & manipulation process and then used with & \\ \cline{3-3} \cline{5-5}  & & & & ASC and TMC VOs and CVOs ontologies & \\ \cline{3-3} \cline{5-5}  & & & & in the data quality analytic model. & \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{40} & \multirow{4}{*}{51,172.8} & \multirow{4}{*}{23,823,072} & In the third case, 40\% random samples of & \\  & & & & data dataset A and B have been & \\ \cline{3-3} \cline{5-5}  & & & & manipulated with respect to data quality & \\ \cline{3-3} \cline{5-5}  & & & & analytic metric parameters and then & \\ \cline{3-3} \cline{5-5}  & & & & harmonized with VOs and CVOs & \\ \cline{3-3} \cline{5-5}  & & & & ontologies for further data quality & \\ \cline{3-3} \cline{5-5}  & & & & analytics using deep learning model. & \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{60} & \multirow{4}{*}{76,759.2} & \multirow{4}{*}{35,734,608} & Similarly, to analyse VOs and CVOs data & \\  & & & & quality, we manipulate dataset A and B & \\ \cline{3-3} \cline{5-5}  & & & & data up to 60\% based on the respective & \\ \cline{3-3} \cline{5-5}  & & & & data quality metric parameters. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental test cases datasets detail.