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Referential Integrity Under Uncertain Data

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Advanced Information Systems Engineering (CAiSE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12751))

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Abstract

Together with domain and entity integrity, referential integrity embodies the integrity principles of information systems. While relational databases address applications for data that is certain, modern applications require the handling of uncertain data. In particular, the veracity of big data and the complex integration of data from heterogeneous sources leave referential integrity vulnerable. We apply possibility theory to introduce the class of possibilistic inclusion dependencies. We show that our class inherits good computational properties from relational inclusion dependencies. In particular, we show that the associated implication problem is PSPACE-complete, but fixed-parameter tractable in the input arity. Combined with possibilistic keys and functional dependencies, our framework makes it possible to quantify the degree of trust in entities and relationships.

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Notes

  1. 1.

    http://www-01.ibm.com/software/data/bigdata/.

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Author information

Authors and Affiliations

  1. School of Computer Science, The University of Auckland, Auckland, New Zealand

    Sebastian Link & Ziheng Wei

Authors
  1. Ziheng Wei
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Correspondence to Sebastian Link .

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Editors and Affiliations

  1. The University of Melbourne, Melbourne, VIC, Australia

    Marcello La Rosa

  2. The University of Queensland, St Lucia, QLD, Australia

    Shazia Sadiq

  3. Universitat Politècnica de Catalunya, Barcelona, Spain

    Ernest Teniente

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Link, S., Wei, Z. (2021). Referential Integrity Under Uncertain Data. In: La Rosa, M., Sadiq, S., Teniente, E. (eds) Advanced Information Systems Engineering. CAiSE 2021. Lecture Notes in Computer Science(), vol 12751. Springer, Cham. https://doi.org/10.1007/978-3-030-79382-1_16

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  • DOI: https://doi.org/10.1007/978-3-030-79382-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79381-4

  • Online ISBN: 978-3-030-79382-1

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