Abstract. A data integration system provides the user with a unified view, called global schema, of the data residing at different sources. Users issue their queries against the global schema, and the system computes answers to queries by suitably accessing the sources, through the mapping, i.e., the specification of the relationship between the global schema and the sources. Since sources are in general autonomous subsystems, the information provided by the data at the sources and the mapping is likely not to be consistent with the knowledge expressed by the global schema. Therefore, the question arises of how to interpret user queries in such a situation, i.e., in the presence of data contradicting the global schema and the mapping. In this paper, we provide an in-depth analysis of the problem of dealing with inconsistencies in data integration systems. In this respect, we highlight the central role played by the mapping, and propose a general "mapping-centered" semantics that allows for computing significant answers to user queries even in the presence of inconsistent information. Based on such a semantic analysis, we define a general formal framework for data integration. Then, we argue that our semantic approach formalizes a very reasonable way of handling inconsistency in such systems, since practically all the existing proposals in the literature can be reconstructed in our framework. This allows for comparing and evaluating the different existing proposals.
A first proposal of an unifying framework for CQA in a Data Integration setting is presented in REF ) using first-order logic; it considers different semantics defined by interpreting the mapping assertions between the global and the local schemas of the data integration system.
A Comprehensive Semantic Framework for Data Integration Systems
Journal of Applied Logic
Computer Science