File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Querying and cleaning uncertain data

TitleQuerying and cleaning uncertain data
Authors
KeywordsProbabilistic queries
Quality management
Uncertain databases
Issue Date2009
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 1st International Workshop on Quality of Context (QuaCon 2009), Stuttgart, Germany, 25-26 June 2009. In Lecture Notes in Computer Science, 2009, v. 5786, p. 41-52 How to Cite?
AbstractThe management of uncertainty in large databases has recently attracted tremendous research interest. Data uncertainty is inherent in many emerging and important applications, including location-based services, wireless sensor networks, biometric and biological databases, and data stream applications. In these systems, it is important to manage data uncertainty carefully, in order to make correct decisions and provide high-quality services to users. To enable the development of these applications, uncertain database systems have been proposed. They consider data uncertainty as a "first-class citizen", and use generic data models to capture uncertainty, as well as provide query operators that return answers with statistical confidences. We summarize our work on uncertain databases in recent years. We explain how data uncertainty can be modeled, and present a classification of probabilistic queries (e.g., range query and nearest-neighbor query). We further study how probabilistic queries can be efficiently evaluated and indexed. We also highlight the issue of removing uncertainty under a stringent cleaning budget, with an attempt of generating high-quality probabilistic answers. © 2009 Springer Berlin Heidelberg.
DescriptionLNCS v. 5786 is Proceedings of the 1st International Workshop, QuaCon 2009
Invited Paper
Persistent Identifierhttp://hdl.handle.net/10722/61159
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorCheng, Ren_HK
dc.date.accessioned2010-07-13T03:32:12Z-
dc.date.available2010-07-13T03:32:12Z-
dc.date.issued2009en_HK
dc.identifier.citationThe 1st International Workshop on Quality of Context (QuaCon 2009), Stuttgart, Germany, 25-26 June 2009. In Lecture Notes in Computer Science, 2009, v. 5786, p. 41-52en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/61159-
dc.descriptionLNCS v. 5786 is Proceedings of the 1st International Workshop, QuaCon 2009en_HK
dc.descriptionInvited Paper-
dc.description.abstractThe management of uncertainty in large databases has recently attracted tremendous research interest. Data uncertainty is inherent in many emerging and important applications, including location-based services, wireless sensor networks, biometric and biological databases, and data stream applications. In these systems, it is important to manage data uncertainty carefully, in order to make correct decisions and provide high-quality services to users. To enable the development of these applications, uncertain database systems have been proposed. They consider data uncertainty as a "first-class citizen", and use generic data models to capture uncertainty, as well as provide query operators that return answers with statistical confidences. We summarize our work on uncertain databases in recent years. We explain how data uncertainty can be modeled, and present a classification of probabilistic queries (e.g., range query and nearest-neighbor query). We further study how probabilistic queries can be efficiently evaluated and indexed. We also highlight the issue of removing uncertainty under a stringent cleaning budget, with an attempt of generating high-quality probabilistic answers. © 2009 Springer Berlin Heidelberg.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Scienceen_HK
dc.rightsThe original publication is available at www.springerlink.com-
dc.subjectProbabilistic queriesen_HK
dc.subjectQuality managementen_HK
dc.subjectUncertain databasesen_HK
dc.titleQuerying and cleaning uncertain dataen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailCheng, R:ckcheng@cs.hku.hken_HK
dc.identifier.authorityCheng, R=rp00074en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-642-04559-2_4en_HK
dc.identifier.scopuseid_2-s2.0-70549106589en_HK
dc.identifier.hkuros162402en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70549106589&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume5786 LNCSen_HK
dc.identifier.spage41en_HK
dc.identifier.epage52en_HK
dc.publisher.placeGermanyen_HK
dc.description.otherThe 1st International Workshop on Quality of Context (QuaCon 2009), Stuttgart, Germany, 25-26 June 2009. In Lecture Notes in Computer Science, 2009, v. 5786, p. 41-52-
dc.identifier.scopusauthoridCheng, R=7201955416en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats