dc.contributor.author |
Brown, P |
en |
dc.contributor.author |
Ganesan, J |
en |
dc.contributor.author |
Köhler, H |
en |
dc.contributor.author |
Link, Sebastian |
en |
dc.coverage.spatial |
Gifu, Japan |
en |
dc.date.accessioned |
2017-05-31T02:43:24Z |
en |
dc.date.issued |
2016-01 |
en |
dc.identifier.citation |
International Conference on Conceptual Modeling: ER 2016, Gifu, Japan. Lecture Notes in Computer Science: Conceptual Modeling. Springer Verlag. 9974: 164-179. Jan 2016 |
en |
dc.identifier.isbn |
9783319463964 |
en |
dc.identifier.issn |
0302-9743 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/33187 |
en |
dc.description.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 keys which empowers users to specify lower and upper bounds on the marginal probabilities by which keys should hold in a data set of acceptable quality. Indeed, the bounds help organizations balance the consistency and completeness targets for their data quality. For this purpose, algorithms are established for an agile schema-and data-driven acquisition of the right lower and upper bounds in a given application domain, and for reasoning about these keys. The efficiency of our acquisition framework is demonstrated theoretically and experimentally. |
en |
dc.publisher |
Springer Verlag |
en |
dc.relation.ispartof |
International Conference on Conceptual Modeling: ER 2016 |
en |
dc.relation.ispartofseries |
Lecture Notes in Computer Science: Conceptual Modeling |
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. Details obtained from http://www.sherpa.ac.uk/romeo/issn/0302-9743/ |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.title |
Keys with probabilistic intervals |
en |
dc.type |
Conference Item |
en |
dc.identifier.doi |
10.1007/978-3-319-46397-1_13 |
en |
pubs.begin-page |
164 |
en |
pubs.volume |
9974 |
en |
dc.description.version |
AM - Accepted Manuscript |
en |
dc.rights.holder |
Copyright: Springer Verlag |
en |
pubs.end-page |
179 |
en |
pubs.publication-status |
Published |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Proceedings |
en |
pubs.elements-id |
552119 |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
School of Computer Science |
en |
dc.identifier.eissn |
1611-3349 |
en |
pubs.record-created-at-source-date |
2017-05-31 |
en |