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2017 | OriginalPaper | Chapter

Cardinality Constraints with Probabilistic Intervals

Authors : Tania Katell Roblot, Sebastian Link

Published in: Conceptual Modeling

Publisher: Springer International Publishing

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Abstract

Probabilistic databases accommodate well the requirements of modern applications that produce large volumes of uncertain data from a variety of sources. We propose an expressive class of probabilistic cardinality constraints which empowers users to specify lower and upper bounds on the marginal probabilities by which cardinality constraints should hold in a data set of acceptable quality. The bounds help organizations balance the consistency and completeness targets for their data quality, and provide probabilities on the number of query answers without querying the data. Algorithms are established for an agile schema-driven acquisition of the right lower and upper bounds in a given application domain, and for reasoning about the constraints.

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previous chapter An Experimental Evaluation of the Understanding of Safety Compliance Needs with Models

next chapter Contextual Keys

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Title: Cardinality Constraints with Probabilistic Intervals
Authors: Tania Katell Roblot
Sebastian Link
Publisher: Springer International Publishing
Book: Conceptual Modeling
Print ISBN: 978-3-319-69903-5

Electronic ISBN: 978-3-319-69904-2

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-69904-2_21

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