# Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application
## 4 Web of Objects Based Contextual Data Quality Assessment Model
### Data Quality Assessment for Semantic Data Applications  
To perform the contextual data quality assessment of the semantic data applications, it is necessary that the incoming data should be transformed into the semantic data using dataset ontologies and VOs/CVOs templates. We applied the contextual data quality assessment metric parameters along with data quality functions at the data acquisition, data quality assessment, and service level. The flow diagram of applied data quality metric parameters algorithm and functions is shown in Figure 14. For the development of contextual data quality assessment model, we designed many data repositories and data quality processing modules in order to support modularity and scalability. However, in the pseudo-code, we show a few of them for clarity and understandability. The VOs and CVOs repositories are used to store the templates of VOs and CVOs along with incoming semantic data for the applications. Among these repositories, there is a service repository that contains the application services template. We developed the data quality requirement repository, which contains the contextual data quality requirement for the CVOs and services. Initially the semantic data of CVOs and VOs are given the input to the contextual metric parameters procedure (_contextualMetricParameters_). This procedure receives the two inputs as the list of CVOs (\(\theta\)) along the corresponding list of VOs (\(\Xi\)) in the current context. For each CVO, all the corresponding VOs are extracted from semantic ontology and its semantic data is assessed with various contextual data quality metric parameters. The _checkMissingVal_ method identifies the missing data values in the _VOi_ compulsory fields. To detect the missing attribute in the _VOi_, the method _checkMissingAttr_ has been designed. This method identifies any missing attribute in the incoming data in the VOs. The information about the data attributes is referred through the semantic dataset and VOs ontology. To assess other data quality parameters that are necessary for the contextual data quality assessment  
Figure 13: Appropriate amount of data metric assessment. various methods are designed such as method for the identifying of value in the wrong fields (_ceheckValueIntWF_), detection of duplicate data (_ceheckDupValue_), and identification of missing subject properties (_checkmissingSubProp_), etc. All the contextual metric parameters in the VOs data within the CVOs context are identified and the final contextual metric parameters object (\(\varSigma\)_CVOt_) is created for further analysis with respect to the contextual metrics. The identified contextual data quality metric parameters are filtered by the contextual metric parameter filtration function in order to transport the relevant metric parameters. The _completeness_ procedure takes the input as a contextual metric parameter object (\(\varSigma\)_CVOt_) and the total number of values (\(\varSigma\)_TV_) in _VOi_ in the current context. In order to get the incomplete data values in the _VOi_ the method getInCompletenessFeatures is designed. This method returns all the missing data with respect to the _VOi_ to the \(\varSigma\)Rs and the final completeness score of _VOi_ in the respective CVO is returned as output. The completeness procedure considers the _CVOi_ aggregated data quality as one, therefore the ratio of incomplete data is subtracted from the total quality. In a similar way, the other data quality metric parameters are identified for the value added, relevancy, and appropriate amount of data quality metrics. The output of CVO aggregated data quality metadata is given as the input to the service quality configuration manager. This functional component is used to manage the service execution based on the available CVOs data quality. If the assessed data quality is below the defined threshold, then the service can be executed partially or otherwise. All the procedures shown in the pseudo code have been developed using the microservices pattern.