Referential Integrity Under Uncertain Data

Show simple item record Link, Sebastian Wei, Ziheng
dc.contributor.editor LaRosa, M
dc.contributor.editor Sadiq, S
dc.contributor.editor Teniente, E
dc.coverage.spatial ELECTR NETWORK 2022-10-05T23:21:00Z 2022-10-05T23:21:00Z 2021-06-24
dc.identifier.citation (2021). Lecture notes in computer science, 12751, 265-279.
dc.identifier.isbn 9783030793814
dc.identifier.issn 1611-3349
dc.description.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.
dc.publisher Springer Nature
dc.relation.ispartof 33rd International Conference on Advanced Information Systems Engineering (CAiSE)
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.
dc.rights This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use
dc.subject Science & Technology
dc.subject Technology
dc.subject Computer Science, Artificial Intelligence
dc.subject Computer Science, Information Systems
dc.subject Computer Science, Interdisciplinary Applications
dc.subject Computer Science, Theory & Methods
dc.subject Computer Science
dc.subject Computational complexity
dc.subject Inclusion dependency
dc.subject Possibility theory
dc.subject Reasoning
dc.subject Referential integrity
dc.subject QUALITY
dc.title Referential Integrity Under Uncertain Data
dc.type Conference Item
dc.identifier.doi 10.1007/978-3-030-79382-1_16
pubs.begin-page 265
pubs.volume 12751 2022-09-28T19:32:43Z
dc.rights.holder Copyright: The authors en
pubs.end-page 279
pubs.finish-date 2021-07-02
pubs.publication-status Published
pubs.start-date 2021-06-28
dc.rights.accessrights en
pubs.elements-id 863002 Science School of Computer Science
dc.identifier.eissn 1611-3349
pubs.record-created-at-source-date 2022-09-29 2021-06-24

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