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dc.contributor.authorLi, Yanhong
dc.contributor.authorWang, Meng
dc.contributor.authorDu, Xiaokun
dc.contributor.authorFeng, Yuhe
dc.contributor.authorLuo, Changyin
dc.contributor.authorTian, Shasha
dc.contributor.authorAnjum, Ashiq
dc.contributor.authorZhu, Rongbo
dc.identifier.citationLi, Y., Wang, M., Zhu, R., Anjum, A., Du, X., Feng, Y., Luo, C. and Tian, S., (2019). 'Intelligent augmented keyword search on spatial entities in real-life internet of vehicles'. Future Generation Computer Systems, 94, pp.697-711. DOI: 10.1016/j.future.2018.12.051.en_US
dc.description.abstractInternet of Vehicles (IoV) has attracted wide attention from both academia and industry. Due to the popularity of the geographical devices deployed on the vehicles, a tremendous amount of spatial entities which include spatial information, unstructured information and structured information, are generated every second. This development calls for intelligent augmented spatial keyword queries (ASKQ), which intelligently takes into account the locations, unstructured information (in the form of keyword sets), structured information (in the form of boolean expressions) of 182MinzuAvespatial entities. In this paper, we take the first step to address the issue of processing ASKQ in real traffic networks of IoV environments (ASKQIV) and focus on Top-k ASKQIV queries. To support network distance pruning, keyword pruning, and boolean expression pruning intelligently and simultaneously, a novel hybrid index structure called ASKTI is proposed. Note in the real-life traffic networks of IoV environments, travel cost is not only decided by the network distance, but also decided by some additional travel factors. By considering these additional factors, a combined factor Cftc of each road (edge) in the traffic network of IoV environments is calculated, and weighted network distance is calculated and adopted. Based on ASKTI, an efficient algorithm for Top-k ASKQIV query processing is proposed. Our method can also be extended to handle boolean range ASKQIV Queries and ranking ASKQIV Queries. Finally, simulation experiments on one real traffic network of IoV environments and two synthetic spatial entity sets are conducted. The results show that our ASKTI based method is superior to its competitors.en_US
dc.description.sponsorshipUniversity of Derbyen_US
dc.rightsCC0 1.0 Universal*
dc.subjectIntelligent queryen_US
dc.subjectInternet of vehiclesen_US
dc.subjectTraffic networken_US
dc.subjectBoolean expressionen_US
dc.titleIntelligent augmented keyword search on spatial entities in real-life internet of vehiclesen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.identifier.journalFuture Generation Computer Systemsen_US
dc.source.journaltitleFuture Generation Computer Systems

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CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal