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 toomany 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.
CITATION STYLE
Laurenza, E. (2017). Talk to your neighbour: A belief propagation approach to data fusion. In Advances in Intelligent Systems and Computing (Vol. 456, pp. 303–310). Springer Verlag. https://doi.org/10.1007/978-3-319-42972-4_38
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