Multidatabase query processing with uncertainty in global keys and attribute values

Abstract

Semantic integration and data integration are two main processes that multidatabase systems need to employ in order to support interoperability. Both these processes involve uncertainty when attribute correspondences and global IDs are unknown or imprecise. The role-set approach is a new conceptual framework for data integration in multidatabase systems that maintains the materialization autonomy of local database systems by presenting the answer to a query as a set of sets representing the distinct intersections between the relations corresponding to the various roles played by an entity. In this article, we present an approach for dynamic database integration and query processing in the absence of information about attribute correspondences and global IDs. We define different types of equivalence conditions for the construction of global IDs. We propose a strategy based on ranked role-sets that makes use of an automated semantic integration procedure based on neural networks to determine candidate global IDs. The data integration and query processing steps then produce a number of role-sets, ranked by the similarity of the candidate IDs.