Scalable linkage across location enhanced services

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dc.citation.epage4en_US
dc.citation.spage1en_US
dc.contributor.authorBasık, Fuaten_US
dc.coverage.spatialMunich, Germany
dc.date.accessioned2018-04-12T11:47:14Z
dc.date.available2018-04-12T11:47:14Z
dc.date.issued2017-08en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionConference name: CEUR Workshop Proceedings -Proceedings of the VLDB 2017 PhD Workshop
dc.descriptionDate of Conference: 28 August, 2017
dc.description.abstractIn this work, we investigate methods for merging spatio-temporal usage and entity records across two location-enhanced services, even when the datasets are semantically different. To address both effectiveness and efficiency, we study this linkage problem in two parts: model and framework. First we discuss models, including κ-l diversity- a concept we developed to capture both spatial and temporal diversity aspects of the linkage, and probabilistic linkage. Second, we aim to develop a framework that brings efficient computation and parallelization support for both models of linkage.en_US
dc.identifier.issn1613-0073
dc.identifier.urihttp://hdl.handle.net/11693/37664
dc.language.isoEnglishen_US
dc.publisherCEUR-WSen_US
dc.source.titleCEUR Workshop Proceedingsen_US
dc.subjectEffectiveness and efficienciesen_US
dc.subjectEfficient computationen_US
dc.subjectL diversitiesen_US
dc.subjectLocation-enhanceden_US
dc.subjectParallelizationsen_US
dc.subjectProbabilistic linkageen_US
dc.subjectSpatio temporalen_US
dc.subjectTemporal diversityen_US
dc.titleScalable linkage across location enhanced servicesen_US
dc.typeConference Paperen_US

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