Probabilistic Cardinality Constraints

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Show simple item record Roblot, TK en Link, S en 2016-01-04T21:55:57Z en 2016-01-04T21:55:57Z en 2015 en
dc.identifier.citation CDMTCS Research Reports CDMTCS-481 (2015) en
dc.identifier.issn 1178-3540 en
dc.identifier.uri en
dc.description.abstract Probabilistic databases address well the requirements of an increasing number of modern applications that produce large collections of uncertain data. We propose probabilistic cardinality constraints as a principled tool to control the occurrences of data patterns in probabilistic databases. Our constraints help balance the consistency and completeness targets for the quality of an organization's data, and can be used to predict with which probability a given number of query answers will be returned without actually querying the data. These target applications are unlocked by developing algorithms to reason efficiently about probabilistic cardinality constraints, and to help analysts acquire the marginal probability by which cardinality constraints should hold in a given application domain. For this purpose, we overcome technical challenges to compute Armstrong PC-sketches as succinct data samples that perfectly visualize any given perceptions about these marginal probabilities. en
dc.publisher Department of Computer Science, The University of Auckland, New Zealand en
dc.relation.ispartofseries CDMTCS Research Report Series en
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. en
dc.rights.uri en
dc.source.uri en
dc.title Probabilistic Cardinality Constraints en
dc.type Technical Report en
dc.subject.marsden Fields of Research en
dc.rights.holder The author(s) en
dc.rights.accessrights en

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