Solving conflicts in database fusion with Bayesian networks

Data fusion is a major task in data management. Frequently, different sources store data about the same real-world entities, however with conflicts in the values of their features. Data fusion aims at solving those conflicts in order to obtain a unique global view over those sources. Some solutions to the problem have been proposed in the database literature, yet they have a number of limitations for real cases: for example they leave too many alternatives to users or produce biased results. This paper proposes a novel algorithm for data fusion actually addressing conflict resolution in databases and overcoming some existing limitations.