Abstract
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.
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Notes
- 1.
Notice that for a given \(i\), different candidates for a variable can also derive from the same sensor, in case of duplicate measures.
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Laurenza, E. (2017). Talk to Your Neighbour: A Belief Propagation Approach to Data Fusion. In: Ferraro, M., et al. Soft Methods for Data Science. SMPS 2016. Advances in Intelligent Systems and Computing, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-42972-4_38
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DOI: https://doi.org/10.1007/978-3-319-42972-4_38
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