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dc.contributor.authorZheng, Bolongen_US
dc.contributor.authorZheng, Kaien_US
dc.contributor.authorScheuermann, Peteren_US
dc.contributor.authorZhou, Xiaofangen_US
dc.contributor.authorNguyen, Quoc Viet Hungen_US
dc.contributor.authorLi, Chenliangen_US
dc.date.accessioned2019-05-29T12:34:52Z
dc.date.available2019-05-29T12:34:52Z
dc.date.issued2018en_US
dc.identifier.issn1386-145Xen_US
dc.identifier.doi10.1007/s11280-018-0535-8en_US
dc.identifier.urihttp://hdl.handle.net/10072/380199
dc.description.abstractDriven by the advances in location positioning techniques and the popularity of location sharing services, semantic enriched trajectory data has become unprecedentedly available. While finding relevant Point-of-Interests (PoIs) based on users’ locations and query keywords has been extensively studied in the past years, it is, however, largely untouched to explore the keyword queries in the context of activity trajectory database. In this paper, we study the problem of searching activity trajectories by keywords. Given a set of query keywords, a keyword-oriented query for activity trajectory (KOAT) returns k trajectories that contain the most relevant keywords to the query and yield the least travel effort in the meantime. The main difference between KOAT and conventional spatial keyword queries is that there is no query location in KOAT, which means the search area cannot be localized. To capture the travel effort in the context of query keywords, a novel score function, called spatio-textual ranking function, is first defined. Then we develop a hybrid index structure called GiKi to organize the trajectories hierarchically, which enables pruning the search space by spatial and textual similarity simultaneously. Finally an efficient search algorithm and fast evaluation of the value of spatio-textual ranking function are proposed. In addition, we extend the proposed techniques of KOAT to support range-based query and order sensitive query, which can be applied for more practical applications. The results of our empirical studies based on real check-in datasets demonstrate that our proposed index and algorithms can achieve good scalability.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherSpringeren_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofpagefrom1en_US
dc.relation.ispartofpageto34en_US
dc.relation.ispartofjournalWorld Wide Weben_US
dc.subject.fieldofresearchDatabase Managementen_US
dc.subject.fieldofresearchDistributed Computingen_US
dc.subject.fieldofresearchInformation Systemsen_US
dc.subject.fieldofresearchcode080604en_US
dc.subject.fieldofresearchcode0805en_US
dc.subject.fieldofresearchcode0806en_US
dc.titleSearching activity trajectory with keywordsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dc.type.codeC - Journal Articlesen_US
gro.description.notepublicThis publication has been entered into Griffith Research Online as an Advanced Online Version.en_US
gro.hasfulltextNo Full Text
gro.griffith.authorNguyen, Henry


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