File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Continuous inverse ranking queries in uncertain streams

TitleContinuous inverse ranking queries in uncertain streams
Authors
Issue Date2011
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 23rd International Conference on Scientific and Statistical Database Management (SSDBM 2011), Portland, OR., 20-22 July 2011. In Lecture Notes in Computer Science, 2011, v. 6809, p. 37-54 How to Cite?
AbstractThis paper introduces a scalable approach for continuous inverse ranking on uncertain streams. An uncertain stream is a stream of object instances with confidences, e.g. observed positions of moving objects derived from a sensor. The confidence value assigned to each instance reflects the likelihood that the instance conforms with the current true object state. The inverse ranking query retrieves the rank of a given query object according to a given score function. In this paper we present a framework that is able to update the query result very efficiently, as the stream provides new observations of the objects. We will theoretically and experimentally show that the query update can be performed in linear time complexity. We conduct an experimental evaluation on synthetic data, which demonstrates the efficiency of our approach. © 2011 Springer-Verlag Berlin Heidelberg.
DescriptionThis vol. is the proceedings of SSDBM 2011
Session 1: Ranked Search
Persistent Identifierhttp://hdl.handle.net/10722/152003
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorBernecker, Ten_US
dc.contributor.authorKriegel, HPen_US
dc.contributor.authorMamoulis, Nen_US
dc.contributor.authorRenz, Men_US
dc.contributor.authorZuefle, Aen_US
dc.date.accessioned2012-06-26T06:32:19Z-
dc.date.available2012-06-26T06:32:19Z-
dc.date.issued2011en_US
dc.identifier.citationThe 23rd International Conference on Scientific and Statistical Database Management (SSDBM 2011), Portland, OR., 20-22 July 2011. In Lecture Notes in Computer Science, 2011, v. 6809, p. 37-54en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10722/152003-
dc.descriptionThis vol. is the proceedings of SSDBM 2011-
dc.descriptionSession 1: Ranked Search-
dc.description.abstractThis paper introduces a scalable approach for continuous inverse ranking on uncertain streams. An uncertain stream is a stream of object instances with confidences, e.g. observed positions of moving objects derived from a sensor. The confidence value assigned to each instance reflects the likelihood that the instance conforms with the current true object state. The inverse ranking query retrieves the rank of a given query object according to a given score function. In this paper we present a framework that is able to update the query result very efficiently, as the stream provides new observations of the objects. We will theoretically and experimentally show that the query update can be performed in linear time complexity. We conduct an experimental evaluation on synthetic data, which demonstrates the efficiency of our approach. © 2011 Springer-Verlag Berlin Heidelberg.en_US
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleContinuous inverse ranking queries in uncertain streamsen_US
dc.typeConference_Paperen_US
dc.identifier.emailBernecker, T: bernecker@dbs.ifi.lmu.de-
dc.identifier.emailKriegel, HP: kriegel@dbs.ifi.lmu.de-
dc.identifier.emailMamoulis, N: nikos@cs.hku.hk-
dc.identifier.emailRenz, M: renz@dbs.ifi.lmu.de-
dc.identifier.emailZuefle, A: zuefle@dbs.ifi.lmu.de-
dc.identifier.authorityMamoulis, N=rp00155en_US
dc.description.naturepostprinten_US
dc.identifier.doi10.1007/978-3-642-22351-8_3en_US
dc.identifier.scopuseid_2-s2.0-79961189593en_US
dc.identifier.hkuros190940-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79961189593&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume6809en_US
dc.identifier.spage37en_US
dc.identifier.epage54en_US
dc.publisher.placeGermanyen_US
dc.description.otherThe 23rd International Conference on Scientific and Statistical Database Management (SSDBM 2011), Portland, OR., 20-22 July 2011. In Lecture Notes in Computer Science, 2011, v. 6809, p. 37-54-
dc.identifier.scopusauthoridBernecker, T=24512341500en_US
dc.identifier.scopusauthoridKriegel, HP=7005718994en_US
dc.identifier.scopusauthoridMamoulis, N=6701782749en_US
dc.identifier.scopusauthoridRenz, M=22433777600en_US
dc.identifier.scopusauthoridZuefle, A=25029386800en_US

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats