Abstract
This paper deals with skyline queries in the context of “dirty databases”, i.e., databases that may contain bad quality or suspect data. We assume that each tuple or attribute value of a given dataset is associated with a quality level and we define several extensions of skyline queries that make it possible to take data quality into account when checking whether a tuple is dominated by another. This leads to the computation of different types of gradual (fuzzy) skylines.
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Jaudoin, H., Pivert, O., Smits, G., Thion, V. (2014). Data-Quality-Aware Skyline Queries. In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_56
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DOI: https://doi.org/10.1007/978-3-319-08326-1_56
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08325-4
Online ISBN: 978-3-319-08326-1
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