Show simple item record

dc.contributor.authorRazniewski S
dc.contributor.authorSadiq SW
dc.contributor.authorZhou X
dc.contributor.editorCheema MA
dc.contributor.editorZhang W
dc.contributor.editorChang L
dc.date.accessioned2017-03-16T09:39:19Z
dc.date.available2017-03-16T09:39:19Z
dc.date.issued2016
dc.identifier.isbn978-3-319-46921-8
dc.identifier.issn0302-9743
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-46922-5_33
dc.identifier.urihttp://hdl.handle.net/10863/1797
dc.description.abstractIn big data settings, the data can often be externally sourced with little or no knowledge of its quality. In such settings, users need to be empowered with the capacity to understand the quality of data sets and implications for use, in order to mitigate the risk of making investments in datasets that will not deliver. In this paper we present an approach for detecting the completeness of high volume stream data generated by a large number of data providers. By exploiting the inherent hierarchies within database attributes, we are able to devise an efficient solution for computing query specific completeness, thereby improving user under-standing of implications of using query results based on incomplete data.en_US
dc.language.isoenen_US
dc.publisherADCen_US
dc.relation.ispartofseriesLecture Notes in Computer Science;
dc.rights
dc.titleExploiting Hierarchies for Efficient Detection of Completeness in Stream Dataen_US
dc.typeBook chapteren_US
dc.date.updated2017-03-16T07:22:00Z
dc.publication.titleDatabases Theory and Applications: 27th Australasian Database Conference, ADC 2016, Sydney, NSW, September 28-29, 2016, Proceedings
dc.language.isiEN-GB
dc.description.fulltextnoneen_US


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record