# Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application
## 1 Introduction
[15; 16], have discussed sixty nine data quality metrics and all these may not be used in the context of single application data, some are important for one case that others will be more suitable in another context. Therefore, it is also a significant contribution to identify the required data quality metrics in the application's context and nature of data. The quality of data should be analyzed in the application or business context impartially [17; 18]. Normally the data quality is assessed with some business rules that can be applied to the data. For example, to measure the accuracy aspect of data quality, the percentage of values with errors is divided by the total number of values in a data instance [19]. However, the contextual data quality assessment is a complex mechanism that needs automatic learning procedures to identify many patterns of incorrect or malicious data. To overcome the aforementioned issues, we present a contextual data quality assessment mechanism that supports data quality assessment for web objects healthcare applications by using deep learning models and semantic ontologies. The proposed data quality assessment mechanism is evaluated with two healthcare applications that provide services based on the emotion data received from wearable sensors. The Web of Objects (WoO) is a simple but efficient services platform that supports a robust way to develop application services with data abstraction using Virtual Objects (VOs) and Composite Virtual Objects (CVOs) [20]. It fosters to analyze a very huge amount of data using microservices and VOs and CVOs ontologies [21]. The intrinsic and contextual data quality assessment processing services are distributed and scaled into many microservices to increase the availability of quality data for the semantic data applications [22; 23]. The proposed model supports to analyze data quality with intrinsic and contextual metric parameters at the data acquisition, assessment and service level. The functionality of WoO based data quality assessment mechanism can be generalized and extended to other applications by incorporating data quality requirements and assessment business rules. Moreover, the contributions of this article have been summarized as follows:  
* The data quality requirements have been identified for WoO based healthcare applications with respect to data acquisition, assessment and measurement of the quality of objects. * The contextual data quality assessment metric parameters and functions for WoO have been defined with respect to the data acquisition, data quality assessment level, and service level. * The intrinsic and contextual data quality assessment metric parameters have been presented. * The deep learning model has been developed to perform the WoO based data quality assessment with respect to the contextual aspect of data quality. * We present a comprehensive web objects based data quality assessment model for the healthcare applications. This model is based on semantic ontologies and deep learning models. * To validate the proposed approach of data quality assessment, we developed the Affective Stress Care (ASC) and Teens' Mood Care (TMC) application ontologies. The conceptual map of this study is illustrated in Figure 2. The study background and research gap are covered in Section 2; the conceptual framework of data quality assessment and intrinsic and contextual metric parameters are covered in Section 3; WoO based contextual data quality assessment model is discussed in Section 4; use case and experimental model is elaborated in Section 5; Results, future work, and conclusion have been covered in Sections 6 and 7 respectively.