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dc.contributor.authorchen, Lisi
dc.contributor.authorShang, Shuo
dc.contributor.authorZhang, Zhiwei
dc.contributor.authorCao, Xin
dc.contributor.authorJensen, Christian S.
dc.contributor.authorKalnis,Panos
dc.date.accessioned2019-03-14T14:17:56Z
dc.date.available2019-03-14T14:17:56Z
dc.date.issued2018-10-25
dc.identifier.citationChen L, Shang S, Zhang Z, Cao X, Jensen CS, et al. (2018) Location-Aware Top-k Term Publish/Subscribe. 2018 IEEE 34th International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2018.00073.
dc.identifier.doi10.1109/ICDE.2018.00073
dc.identifier.urihttp://hdl.handle.net/10754/631621
dc.description.abstractMassive amount of data that contain spatial, textual, and temporal information are being generated at a high scale. These spatio-Temporal documents cover a wide range of topics in local area. Users are interested in receiving local popular terms from spatio-Temporal documents published with a specified region. We consider the Top-k Spatial-Temporal Term (ST2) Subscription. Given an ST2 subscription, we continuously maintain up-To-date top-k most popular terms over a stream of spatio-Temporal documents. The ST2 subscription takes into account both frequency and recency of a term generated from spatio-Temporal document streams in evaluating its popularity. We propose an efficient solution to process a large number of ST2 subscriptions over a stream of spatio-Temporal documents. The performance of processing ST2 subscriptions is studied in extensive experiments based on two real spatio-Temporal datasets.
dc.description.sponsorshipThis work was supported by the grant of the Hong Kong Research Grants Council, Hong Kong SAR, China, No. 12258116 and the Nation Nature Science Foundation of China, China, No. 61602395.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8509294
dc.subjectpublish
dc.subjectSpatial
dc.subjectstream
dc.subjectSubscribe
dc.subjectTemporal
dc.titleLocation-Aware Top-k Term Publish/Subscribe
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journal2018 IEEE 34th International Conference on Data Engineering (ICDE)
dc.conference.date2018-04-16 to 2018-04-19
dc.conference.name34th IEEE International Conference on Data Engineering, ICDE 2018
dc.conference.locationParis, FRA
dc.contributor.institutionUniversity of Wollongong, , United States
dc.contributor.institutionHong Kong Baptist University, , Hong Kong
dc.contributor.institutionUniversity of New South Wales, , Australia
dc.contributor.institutionAalborg University, , Denmark
kaust.personShang, Shuo
dc.date.published-online2018-10-25
dc.date.published-print2018-04


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