Abstract
This paper introduces probabilistic databases with unmerged duplicates (DBud), i.e., databases containing probabilistic information about instances found to describe the same real-world objects. We discuss the need for efficiently querying such databases and for supporting practical query scenarios that require analytical or summarized information. We also sketch possible methodologies and techniques that would allow performing efficient processing of queries over such probabilistic databases, and especially without the need to materialize the (potentially, huge) collection of all possible deduplication worlds.
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© 2014 Springer International Publishing Switzerland
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Ioannou, E., Garofalakis, M. (2014). Analytics over Probabilistic Unmerged Duplicates. In: Straccia, U., Calì, A. (eds) Scalable Uncertainty Management. SUM 2014. Lecture Notes in Computer Science(), vol 8720. Springer, Cham. https://doi.org/10.1007/978-3-319-11508-5_17
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DOI: https://doi.org/10.1007/978-3-319-11508-5_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11507-8
Online ISBN: 978-3-319-11508-5
eBook Packages: Computer ScienceComputer Science (R0)