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dc.contributor.authorchen, Lisi
dc.contributor.authorShang, Shuo
dc.contributor.authorYao, Bin
dc.contributor.authorZheng, Kai
dc.date.accessioned2018-10-03T07:41:02Z
dc.date.available2018-10-03T07:41:02Z
dc.date.issued2018-06-18
dc.identifier.citationChen L, Shang S, Yao B, Zheng K (2018) Spatio-temporal top-k term search over sliding window. World Wide Web. Available: http://dx.doi.org/10.1007/s11280-018-0606-x.
dc.identifier.issn1386-145X
dc.identifier.issn1573-1413
dc.identifier.doi10.1007/s11280-018-0606-x
dc.identifier.urihttp://hdl.handle.net/10754/628866
dc.description.abstractIn part due to the proliferation of GPS-equipped mobile devices, massive volumes of geo-tagged streaming text messages are becoming available on social media. It is of great interest to discover most frequent nearby terms from such tremendous stream data. In this paper, we present novel indexing, updating, and query processing techniques that are capable of discovering top-k most frequent nearby terms over a sliding window. Specifically, given a query location and a set of geo-tagged messages within a sliding window, we study the problem of searching for the top-k terms by considering term frequency, spatial proximity, and term freshness. We develop a novel and efficient mechanism to solve the problem, including a quad-tree based indexing structure, indexing update technique, and a best-first based searching algorithm. An empirical study is conducted to show that our proposed techniques are efficient and fit for users’ requirements through varying a number of parameters.
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/article/10.1007/s11280-018-0606-x
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s11280-018-0606-x
dc.subjectSpatial
dc.subjectTemporal
dc.subjectTerm
dc.subjectTop-k
dc.titleSpatio-temporal top-k term search over sliding window
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalWorld Wide Web
dc.eprint.versionPost-print
dc.contributor.institutionUniversity of Wollongong, Wollongong, , Australia
dc.contributor.institutionShanghai Jiao Tong University, Shanghai, , China
dc.contributor.institutionUniversity of Electronic Science and Technology of China, Chengdu, , China
kaust.personShang, Shuo
refterms.dateFOA2018-10-03T11:18:36Z


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