# Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application
## 6 Result and Discussion
quality assessment model average f-measure, MSE, RMSE, and PCC were 0.98, 0.00079, 0.0089, and 0.97 respectively on both applications (i.e., ASC and TMC) semantic data quality assessment. The data quality assessment learning model has been implemented using TensorFlow. In the experimental model, we used a machine with configuration including Intel Core i7 with 3.4 GHz clocked, 32 GB RAM and NVIDIA GeForce GTX configuration. The contextual data quality assessment has been analyzed with aggregated data assessment functions. In the contextual data quality analytics, the VOs (VO_BVP, VO_ECG, VO_Skin_temp, and VO_RES) data have been aggregated and harmonized in a Stress_monitoring_CVO and Mood_monitoring_CVO context by using aggregated assessment function. Therefore, this type of assessment may also be called as aggregated assessment. In Figure 26 the data values at the x-axis show the model prediction capability with respect to four test cases for the ASC application CVOs with semantic data from dataset A and B. For the contextual completeness assessment, the Stress_monitoring_CVO with four VOs data has been analyzed. In the test case one, VO_BVP, VO_ECG, VO_Skin_temp, and VO_RES data have been considered as 100% complete and we received the model learning capability 97% and 90% with dataset A and B respectively. In the second test case, the Stress_monitoring_CVO data reduced to 90% by manipulating 10% of data from all the relevant VOs. In this case, we received 94% and 85.5% model learning capability with dataset A and B respectively. During the third case, the Stress_monitoring_CVO completeness remains at 60% because we intentionally add 40% invalid data with respect to metric parameters. And in this case, we received 90% and 80% results. In the last fourth case, the Stress_monitoring_CVO completeness model learning capability remains at 43% and 35.2% on dataset A and B respectively. The TMC application's Mood_monitoring_CVO has been analyzed in the contextual data quality assessment. In the TMC Mood_monitoring_CVO context, VOs (Plus_VO, Skin_temperature_VO, Heart_rate_VO, and Facial_muscle_VO, Blood_presure_VO) data have been aggregated and analyzed simultaneously with a deep learning model. The relevant VOs data for a CVO have been distributed into four test cases as we did for the ASC application, however this time the application ontology is different. As shown in Figure 27, during the contextual analytics we received the highest classification capability as 95.2% and 88.1% with semantic data from dataset A and B respectively. In this case, we used the master dataset along with VOs (Plus_VO, Skin_temperature_VO, Heart_rate_VO, Facial_muscle_VO, and Blood_presure_VO) in the context of Mood_monitoring_CVO ontology. In the second case, the deep learning model was able to detect the Mood_monitoring_CVO completeness with 91.3% and 82.3% on dataset A and B respectively. In the last test case, the model only received detection capability of 39.6% and 32.1% on dataset A and B respectively. Hence, it has been observed that when there are many invalid data quality metric parameters of completeness metric, the contextual quality of Mood_monitoring_CVO has been decreased significantly. Figure 26: ASC application contextual data quality assessment with respect to completeness aspect. In the second experiment, we applied the appropriate amount of data quality metric parameters and create the representation of semantic data input to the deep learning model. To create the features and data representation of this metric, we applied the aggregated data quality assessment function over the VOs semantic data that have been composed in the CVO context. We also analyzed this metric with four test cases and the average results on both datasets are shown in Figure 28. In this experiment, two sets of CVOs for ASC and TMC applications have been created. The first set of CVOs includes four VOs and the second one received data from the three VOs.