Hybrid Context-Aware Method for Quality Assessment of Data Streams

Data quality is one of the most important issues that if not taken into consideration appropriately, results in the low reliability of the knowledge extracted through big data analytics. Furthermore, the challenges with data quality management are even greater with streaming data. Most of the methods introduced in the literature for processing streaming data do not use contextual information for the purpose of addressing data quality issues, however, it is possible to improve the performance of these methods by considering the contextual information, especially those obtained from the external resources. Based on this point of view, our main objective in this thesis is to propose a hybrid multivariate context-aware approach for data quality assessment in streaming environments, such as smart city applications.