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dc.contributor.authorBamba, B.en_US
dc.contributor.authorWu, K.-L.en_US
dc.contributor.authorGedik, Buğraen_US
dc.contributor.authorLiu L.en_US
dc.coverage.spatialSanta Clara, CA, USAen_US
dc.date.accessioned2016-02-08T12:10:10Z
dc.date.available2016-02-08T12:10:10Z
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/11693/28067
dc.descriptionDate of Conference: 28 June-3 July 2013en_US
dc.description.abstractLocation-sensitive information monitoring services are a centerpiece of the technology for disseminating content-rich information from massive data streams to mobile users. The key challenges for such monitoring services are characterized by the combination of spatial and non-spatial attributes being monitored and the wide spectrum of update rates. A typical example of such services is "alert me when the gas price at a gas station within 5 miles of my current location drops to $4 per gallon". Such a service needs to monitor the gas price changes in conjunction with the highly dynamic nature of location information. Scalability of such location sensitive and content rich information monitoring services in the presence of different update rates and monitoring thresholds poses a big technical challenge. In this paper, we present SLIM, a scalable location sensitive information monitoring service framework with two unique features. First, we make intelligent use of the correlation between spatial and non-spatial attributes involved in the information monitoring service requests to devise a highly scalable distributed spatial trigger evaluation engine. Second, we introduce single and multi-dimensional safe value containment techniques to efficiently perform selective distributed processing of spatial triggers to reduce the amount of unnecessary trigger evaluations. Through extensive experiments, we show that SLIM offers high scalability for location-sensitive, content-rich information monitoring services in terms of the number of information sources being monitored, number of users and monitoring requests. © 2013 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.title2013 IEEE 20th International Conference on Web Servicesen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICWS.2013.17en_US
dc.subjectInformation monitoringen_US
dc.subjectProactive location-based servicesen_US
dc.subjectSpatial triggersen_US
dc.subjectLocation based servicesen_US
dc.subjectScalabilityen_US
dc.subjectWeb servicesen_US
dc.subjectWebsitesen_US
dc.subjectDistributed processingen_US
dc.subjectInformation monitoringen_US
dc.subjectLocation informationen_US
dc.subjectLocation-sensitive informationen_US
dc.subjectMassive data streamsen_US
dc.subjectNon-spatial attributesen_US
dc.subjectSpatial triggersen_US
dc.subjectTechnical challengesen_US
dc.subjectMonitoringen_US
dc.titleSLIM: A scalable location-sensitive information monitoring serviceen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage50en_US
dc.citation.epage57en_US
dc.identifier.doi10.1109/ICWS.2013.17en_US
dc.publisherIEEEen_US


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