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International Conference on Conceptual Modeling

ER 2016: Conceptual Modeling pp 133–148Cite as

Possibilistic Cardinality Constraints and Functional Dependencies

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9974))

Abstract

Cardinality constraints and functional dependencies together can express many semantic properties for applications in which data is certain. However, modern applications need to process large volumes of uncertain data. So far, cardinality constraints and functional dependencies have only been studied in isolation over uncertain data. We investigate the more challenging real-world case in which both types of constraints co-occur. While more expressive constraints could easily be defined, they would not enjoy the computational properties we show to hold for our combined class. Indeed, we characterize the associated implication problem axiomatically and algorithmically in linear input time. We also show how to summarize any given set of our constraints as an Armstrong instance. These instances help data analysts consolidate meaningful degrees of certainty by which our constraints hold in the underlying application domain.

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Acknowledgement

This research is supported by the Marsden fund council from Government funding, administered by the Royal Society of New Zealand.

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Authors and Affiliations

  1. Department of Computer Science, University of Auckland, Auckland, New Zealand

    Tania K. Roblot & Sebastian Link

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  1. Tania K. Roblot
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Correspondence to Sebastian Link .

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Editors and Affiliations

  1. CEDRIC-CNAM & ESSEC Business School, Cergy-Pontoise, France

    Isabelle Comyn-Wattiau

  2. Kyoto University , Kyoto, Japan

    Katsumi Tanaka

  3. Drexel University , Philadelphia, Pennsylvania, USA

    Il-Yeol Song

  4. Nagoya University , Nagoya, Japan

    Shuichiro Yamamoto

  5. Tokyo Institute of Technology , Tokyo, Japan

    Motoshi Saeki

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Roblot, T.K., Link, S. (2016). Possibilistic Cardinality Constraints and Functional Dependencies. In: Comyn-Wattiau, I., Tanaka, K., Song, IY., Yamamoto, S., Saeki, M. (eds) Conceptual Modeling. ER 2016. Lecture Notes in Computer Science(), vol 9974. Springer, Cham. https://doi.org/10.1007/978-3-319-46397-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-46397-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46396-4

  • Online ISBN: 978-3-319-46397-1

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