The issue of data quality is increasingly important as individuals as well as corporations are relying on multiple, often external sources of data to make decisions. Traditional query systems do not consider data quality in their response. Further studies into the diverse interpretations of data quality indicate that fitness for use is a fundamental criterion in the evaluation of data quality. In this thesis the issue of data quality aware query systems is addressed by developing a query-answering framework that considers user data quality preferences over proposed collaborative systems architecture. Our work is motivated by an extensive study of DQ literature, considering a broad range of Information System (IS) and Computer Science (CS) publication (conference and journal) outlets, in order to understand the current landscape of data quality research. Our investigation revealed a lack of holistic solutions that encompass both business users as well as the technological aspect of data quality management. Accordingly, the developed framework for data quality aware query systems focused on three major issues relating to quality aware query answering, namely measuring data source quality, modelling of user data quality preferences, and answering the query considering those preferences and measures. We then address each of these issues by introducing Data Quality profiling, Data Quality Aware SQL, and Data Quality Aware Query Answering methods respectively. The contributions of this thesis have been evaluated on real and simulated data. The individual components have also been assembled into a running prototype.