When users search the Web it is difficult to return information that is as complete as possible. Also many short texts, such as tweets, are not self-contained and need to be explained since understanding them requires additional knowledge or related facts. Recent research focuses on the context of the user, not on complementing the search results, seeking to return more specific information for the search. Unlike previous works, this research focuses on the context of the retrieved data, attempting to enrich the returned information by incorporating complementary facts that give context to the information requested by the user.

The integration of software systems running in different healthcare organizations is increasingly required in order exchange data and carrying out healthcare processes in coordinated ways. This integration is usually supported by integration platforms which are based on advanced middleware technologies, like the Enterprise Service Bus (ESB). In addition, data quality in the healthcare area is critical to take timely and accurate decisions for the health and well-being of patients. This paper proposes a data quality aware ESB (DQ-ESB) for e-Health integration platforms which allows assessing and enforcing data quality within inter-organizational data exchanges. DQ-ESB handles an extensible data quality model which includes quality characteristics identified within the Uruguayan e-Health context. DQ-ESB provides data quality solutions based on native ESB mechanisms (e.g. data transformation) and it was implemented leveraging the Switchyard ESB product.

Many researchers have presented the need to incorporate and maintain data quality in Data Warehousing Systems. However, there is no consensus in the research community on how to do it. On the other hand, data loaded into the Data Warehouse come from different sources with different levels of quality. Analysis domains of these data can vary and users can perceive the quality in different ways, depending on their profile, the task to be performed, etc. Data quality depends on multiple factors: the sources, the task to be performed with the data, user preferences, etc. Hence, data quality depends on the context in which these data will be used. For this reason, we present a proposal to assess data quality in Data Warehousing Systems with an approach based on Contexts.